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"normalizedAbstract": "Augmented reality (AR) may be a useful technique to overcome issues of conventionally used navigation systems supporting medical needle insertions, like increased mental workload and complicated hand-eye coordination. Previous research primarily focused on the development of AR navigation systems designed for specific displaying devices, but differences between employed methods have not been investigated before. To this end, a user study involving a needle insertion task was conducted comparing different AR display techniques with a monitor-based approach as baseline condition for the visualization of navigation information. A video see-through stationary display, an optical see-through head-mounted display and a spatial AR projector-camera-system were investigated in this comparison. Results suggest advantages of using projected navigation information in terms of lower task completion time, lower angular deviation and affirmative subjective participant feedback. Techniques requiring the intermediate view on screens, i.e. the stationary display and the baseline condition, showed less favorable results. Thus, benefits of providing AR navigation information compared to a conventionally used method could be identified. Significant objective measures results, as well as an identification of advantages and disadvantages of individual display techniques contribute to the development and design of improved needle navigation systems.",
"title": "Comparison of Augmented Reality Display Techniques to Support Medical Needle Insertion",
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"Augmented Reality",
"Data Visualisation",
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"Needles",
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"Needle Insertion Task",
"Stationary Display",
"Head Mounted Display",
"Projector Camera System",
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"Task Completion Time",
"Angular Deviation",
"AR Navigation Information",
"Conventionally Used Method",
"Needle Navigation Systems",
"AR Display Techniques",
"AR Navigation Systems",
"Hand Eye Coordination",
"Mental Workload",
"Medical Needle Insertion",
"Augmented Reality Display Techniques",
"Navigation",
"Augmented Reality",
"Biomedical Monitoring",
"Navigation",
"Data Visualization",
"Phantoms",
"Optical Imaging",
"Medical Augmented Reality",
"Display Techniques",
"Surgical Navigation Systems",
"Needle Guidance",
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],
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"surname": "Heinrich",
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"affiliation": "University of Magdeburg, Research Campus STIMULATE, Germany",
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{
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"givenName": "Fabian",
"surname": "Joeres",
"fullName": "Fabian Joeres",
"affiliation": "University of Magdeburg, Research Campus STIMULATE, Germany",
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"givenName": "Kai",
"surname": "Lawonn",
"fullName": "Kai Lawonn",
"affiliation": "University of Jena",
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{
"givenName": "Christian",
"surname": "Hansen",
"fullName": "Christian Hansen",
"affiliation": "University of Magdeburg, Research Campus STIMULATE, Germany",
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"abstract": "Recent advances in deep neural networks have substantially improved the accuracy and speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference imposes a heavy computation burden on end devices, but offloading inference tasks to the cloud causes a large volume of data transmission. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we designed the DNN surgery, which allows partitioned DNN to be processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph rather than a chain, so that partition is incredibly complicated. To solve the issues, We design a Dynamic Adaptive DNN Surgery(DADS) scheme, which optimally partitions the DNN under different network conditions. We also study the partition problem under the cost-constrained system, where the resource of the cloud for inference is limited. Then, a real-world prototype based on the selif-driving car video dataset is implemented, showing that compared with current approaches, DNN surgery can improve latency up to 6.45 times and improve throughput up to 8.31 times. We further evaluate DNN surgery through two case studies where we use DNN surgery to support an indoor intrusion detection application and a campus traffic monitor application, and DNN surgery shows consistently high throughput and low latency.",
"abstracts": [
{
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"content": "Recent advances in deep neural networks have substantially improved the accuracy and speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference imposes a heavy computation burden on end devices, but offloading inference tasks to the cloud causes a large volume of data transmission. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we designed the DNN surgery, which allows partitioned DNN to be processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph rather than a chain, so that partition is incredibly complicated. To solve the issues, We design a Dynamic Adaptive DNN Surgery(DADS) scheme, which optimally partitions the DNN under different network conditions. We also study the partition problem under the cost-constrained system, where the resource of the cloud for inference is limited. Then, a real-world prototype based on the selif-driving car video dataset is implemented, showing that compared with current approaches, DNN surgery can improve latency up to 6.45 times and improve throughput up to 8.31 times. We further evaluate DNN surgery through two case studies where we use DNN surgery to support an indoor intrusion detection application and a campus traffic monitor application, and DNN surgery shows consistently high throughput and low latency.",
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"normalizedAbstract": "Recent advances in deep neural networks have substantially improved the accuracy and speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference imposes a heavy computation burden on end devices, but offloading inference tasks to the cloud causes a large volume of data transmission. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we designed the DNN surgery, which allows partitioned DNN to be processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph rather than a chain, so that partition is incredibly complicated. To solve the issues, We design a Dynamic Adaptive DNN Surgery(DADS) scheme, which optimally partitions the DNN under different network conditions. We also study the partition problem under the cost-constrained system, where the resource of the cloud for inference is limited. Then, a real-world prototype based on the selif-driving car video dataset is implemented, showing that compared with current approaches, DNN surgery can improve latency up to 6.45 times and improve throughput up to 8.31 times. We further evaluate DNN surgery through two case studies where we use DNN surgery to support an indoor intrusion detection application and a campus traffic monitor application, and DNN surgery shows consistently high throughput and low latency.",
"title": "DNN Surgery: Accelerating DNN Inference on the Edge through Layer Partitioning",
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{
"givenName": "Huanghuang",
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"affiliation": "School of Computer Science, Wuhan University, Hubei, China",
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"surname": "Sang",
"fullName": "Qianlong Sang",
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"surname": "Hu",
"fullName": "Chuang Hu",
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"fullName": "Dazhao Cheng",
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{
"givenName": "Xiaobo",
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"abstract": "Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the performance of accuracy and robustness before they can be deployed to autonomous vehicles. In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization, and the semantic adversarial learning efficiently exposes interpretable robustness risks and enables minimal human interaction for actionable insights. We also demonstrate the effectiveness of various performance improvement strategies derived from actionable insights with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the performance of accuracy and robustness before they can be deployed to autonomous vehicles. In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization, and the semantic adversarial learning efficiently exposes interpretable robustness risks and enables minimal human interaction for actionable insights. We also demonstrate the effectiveness of various performance improvement strategies derived from actionable insights with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.",
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"normalizedAbstract": "Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the performance of accuracy and robustness before they can be deployed to autonomous vehicles. In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization, and the semantic adversarial learning efficiently exposes interpretable robustness risks and enables minimal human interaction for actionable insights. We also demonstrate the effectiveness of various performance improvement strategies derived from actionable insights with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.",
"title": "<italic>VATLD</italic>: A <italic>V</italic>isual <italic>A</italic>nalytics System to Assess, Understand and Improve <italic>T</italic>raffic <italic>L</italic>ight <italic>D</italic>etection",
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"abstract": "View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenges are how to aggregate multi-view features and deal with 3D shapes in arbitrary poses. We propose two versions of a novel view-based Graph Convolutional Network, dubbed view-GCN and view-GCN++, to recognize 3D shape based on graph representation of multiple views. We first construct view-graph with multiple views as graph nodes, then design two graph convolutional networks over the view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. Specifically, view-GCN is a hierarchical network based on two pivotal operations, i.e., feature transform based on local positional and non-local graph convolution, and graph coarsening based on a selective view-sampling operation. To deal with rotation sensitivity, we further propose view-GCN++ with local attentional graph convolution operation and rotation robust view-sampling operation for graph coarsening. By these designs, view-GCN++ achieves invariance to transformations under the finite subgroup of rotation group SO(3). Extensive experiments on benchmark datasets (i.e., ModelNet40, ScanObjectNN, RGBD and ShapeNet Core55) show that view-GCN and view-GCN++ achieve state-of-the-art results for 3D shape classification and retrieval tasks under aligned and rotated settings.",
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"content": "View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenges are how to aggregate multi-view features and deal with 3D shapes in arbitrary poses. We propose two versions of a novel view-based Graph Convolutional Network, dubbed view-GCN and view-GCN++, to recognize 3D shape based on graph representation of multiple views. We first construct view-graph with multiple views as graph nodes, then design two graph convolutional networks over the view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. Specifically, view-GCN is a hierarchical network based on two pivotal operations, i.e., feature transform based on local positional and non-local graph convolution, and graph coarsening based on a selective view-sampling operation. To deal with rotation sensitivity, we further propose view-GCN++ with local attentional graph convolution operation and rotation robust view-sampling operation for graph coarsening. By these designs, view-GCN++ achieves invariance to transformations under the finite subgroup of rotation group SO(3). Extensive experiments on benchmark datasets (i.e., ModelNet40, ScanObjectNN, RGBD and ShapeNet Core55) show that view-GCN and view-GCN++ achieve state-of-the-art results for 3D shape classification and retrieval tasks under aligned and rotated settings.",
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"abstract": "Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on total variation for removing impulse noise in image restoration. This type of noise frequently arises in data acquisition and transmission due to many reasons, e.g., a faulty sensor or analog-to-digital converter errors. Removing this noise is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref> , which is based on TV with <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{02}$_Z</tex-math></inline-formula>-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new sparse optimization method, called <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0TV$_Z</tex-math></inline-formula>-PADMM, which solves the TV-based restoration problem with <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0$_Z</tex-math></inline-formula>-norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent biconvex Mathematical Program with Equilibrium Constraints (MPEC), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0TV$_Z</tex-math></inline-formula>-PADMM method finds a desirable solution to the original <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0$_Z</tex-math></inline-formula>-norm optimization problem and is proven to be convergent under mild conditions. We apply <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0TV$_Z</tex-math></inline-formula>-PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _0TV$_Z</tex-math></inline-formula>-PADMM outperforms state-of-the-art image restoration methods.",
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"content": "Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on total variation for removing impulse noise in image restoration. This type of noise frequently arises in data acquisition and transmission due to many reasons, e.g., a faulty sensor or analog-to-digital converter errors. Removing this noise is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref> , which is based on TV with <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{02}$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq2-2783936.gif\"/></alternatives></inline-formula>-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new sparse optimization method, called <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0TV$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq3-2783936.gif\"/></alternatives></inline-formula>-PADMM, which solves the TV-based restoration problem with <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq4-2783936.gif\"/></alternatives></inline-formula>-norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent biconvex Mathematical Program with Equilibrium Constraints (MPEC), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0TV$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq5-2783936.gif\"/></alternatives></inline-formula>-PADMM method finds a desirable solution to the original <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq6-2783936.gif\"/></alternatives></inline-formula>-norm optimization problem and is proven to be convergent under mild conditions. We apply <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0TV$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq7-2783936.gif\"/></alternatives></inline-formula>-PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that <inline-formula><tex-math notation=\"LaTeX\">$\\ell _0TV$</tex-math><alternatives><inline-graphic xlink:href=\"yuan-ieq8-2783936.gif\"/></alternatives></inline-formula>-PADMM outperforms state-of-the-art image restoration methods.",
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However, both of them have some limitations: a top-<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq3-3166835.gif\"/></alternatives></inline-formula> query requires the user to provide her utility functions for finding the <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq4-3166835.gif\"/></alternatives></inline-formula> tuples with the highest scores as the result; a skyline query does not need any user-specified utility function but cannot control the result size. To overcome their drawbacks, the <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq5-3166835.gif\"/></alternatives></inline-formula>-regret minimization query was proposed and received much attention recently, since it does not require any user-specified utility function and returns a fixed-size result set. Specifically, it selects a set <inline-formula><tex-math notation=\"LaTeX\">$R$</tex-math><alternatives><mml:math><mml:mi>R</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq6-3166835.gif\"/></alternatives></inline-formula> of tuples with a pre-defined size <inline-formula><tex-math notation=\"LaTeX\">$r$</tex-math><alternatives><mml:math><mml:mi>r</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq7-3166835.gif\"/></alternatives></inline-formula> from a database <inline-formula><tex-math notation=\"LaTeX\">$D$</tex-math><alternatives><mml:math><mml:mi>D</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq8-3166835.gif\"/></alternatives></inline-formula> such that the <italic>maximum <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq9-3166835.gif\"/></alternatives></inline-formula>-regret ratio</italic>, which captures how well the top-ranked tuple in <inline-formula><tex-math notation=\"LaTeX\">$R$</tex-math><alternatives><mml:math><mml:mi>R</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq10-3166835.gif\"/></alternatives></inline-formula> represents the top-<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq11-3166835.gif\"/></alternatives></inline-formula> tuples in <inline-formula><tex-math notation=\"LaTeX\">$D$</tex-math><alternatives><mml:math><mml:mi>D</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq12-3166835.gif\"/></alternatives></inline-formula> for any possible utility function, is minimized. Although there have been many methods for <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq13-3166835.gif\"/></alternatives></inline-formula>-regret minimization query processing, most of them are designed for static databases without tuple insertions and deletions. The only known algorithm to process continuous <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq14-3166835.gif\"/></alternatives></inline-formula>-regret minimization queries (C<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq15-3166835.gif\"/></alternatives></inline-formula>RMQ) in dynamic databases suffers from suboptimal approximation and high time complexity. In this paper, we propose a novel dynamic coreset-based approach, called <sc>DynCore</sc>, for C<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq16-3166835.gif\"/></alternatives></inline-formula>RMQ processing. It achieves the same (asymptotically optimal) upper bound on the maximum <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq17-3166835.gif\"/></alternatives></inline-formula>-regret ratio as the best-known static algorithm. Meanwhile, its time complexity is sublinear to the database size, which is significantly lower than that of the existing dynamic algorithm. The efficiency and effectiveness of <sc>DynCore</sc> is confirmed by experimental results on real-world and synthetic datasets.",
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"normalizedAbstract": "Finding a small set of representative tuples from a large database is an important functionality for supporting multi-criteria decision making. Top-- queries and skyline queries are two widely studied queries to fulfill this task. However, both of them have some limitations: a top-- query requires the user to provide her utility functions for finding the - tuples with the highest scores as the result; a skyline query does not need any user-specified utility function but cannot control the result size. To overcome their drawbacks, the --regret minimization query was proposed and received much attention recently, since it does not require any user-specified utility function and returns a fixed-size result set. Specifically, it selects a set - of tuples with a pre-defined size - from a database - such that the maximum --regret ratio, which captures how well the top-ranked tuple in - represents the top-- tuples in - for any possible utility function, is minimized. Although there have been many methods for --regret minimization query processing, most of them are designed for static databases without tuple insertions and deletions. The only known algorithm to process continuous --regret minimization queries (C-RMQ) in dynamic databases suffers from suboptimal approximation and high time complexity. In this paper, we propose a novel dynamic coreset-based approach, called DynCore, for C-RMQ processing. It achieves the same (asymptotically optimal) upper bound on the maximum --regret ratio as the best-known static algorithm. Meanwhile, its time complexity is sublinear to the database size, which is significantly lower than that of the existing dynamic algorithm. The efficiency and effectiveness of DynCore is confirmed by experimental results on real-world and synthetic datasets.",
"title": "Continuous <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-Regret Minimization Queries: A Dynamic Coreset Approach",
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"affiliation": "College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China",
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"givenName": "Xiaoyang",
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"doi": "10.1109/TPAMI.2022.3179556",
"abstract": "The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten-<inline-formula><tex-math notation=\"LaTeX\">Z_$p$_Z</tex-math></inline-formula> norm (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten-<inline-formula><tex-math notation=\"LaTeX\">Z_$p$_Z</tex-math></inline-formula> norm minimization (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM)-based multi-view subspace clustering (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM-MSC) model is proposed. Specially, the proposed <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM-MSC model where the challenging <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{TLS}_{p}$_Z</tex-math></inline-formula>NM-MSC model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten-<inline-formula><tex-math notation=\"LaTeX\">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href=\"sun-ieq2-3179556.gif\"/></alternatives></inline-formula> norm (<inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq3-3179556.gif\"/></alternatives></inline-formula>N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten-<inline-formula><tex-math notation=\"LaTeX\">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href=\"sun-ieq4-3179556.gif\"/></alternatives></inline-formula> norm minimization (<inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq5-3179556.gif\"/></alternatives></inline-formula>NM)-based multi-view subspace clustering (<inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq6-3179556.gif\"/></alternatives></inline-formula>NM-MSC) model is proposed. Specially, the proposed <inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq7-3179556.gif\"/></alternatives></inline-formula>NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block <inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq8-3179556.gif\"/></alternatives></inline-formula>NM-MSC model where the challenging <inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq9-3179556.gif\"/></alternatives></inline-formula>NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the <inline-formula><tex-math notation=\"LaTeX\">$\\text{TLS}_{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>TLS</mml:mtext><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"sun-ieq10-3179556.gif\"/></alternatives></inline-formula>NM-MSC model.",
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"normalizedAbstract": "The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten-- norm (-N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten-- norm minimization (-NM)-based multi-view subspace clustering (-NM-MSC) model is proposed. Specially, the proposed -NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block -NM-MSC model where the challenging -NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the -NM-MSC model.",
"title": "Logarithmic Schatten-<inline-formula><tex-math notation=\"LaTeX\">Z_$p$_Z</tex-math></inline-formula> Norm Minimization for Tensorial Multi-View Subspace Clustering",
"normalizedTitle": "Logarithmic Schatten-- Norm Minimization for Tensorial Multi-View Subspace Clustering",
"fno": "09786656",
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"keywords": [
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"authors": [
{
"givenName": "Jipeng",
"surname": "Guo",
"fullName": "Jipeng Guo",
"affiliation": "Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China",
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{
"givenName": "Yanfeng",
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"abstract": "<inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means method using Lloyd heuristic is a traditional clustering method which has played a key role in multiple downstream tasks of machine learning because of its simplicity. However, Lloyd heuristic always finds a bad local minimum, i.e., the bad local minimum makes objective function value not small enough, which limits the performance of <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means. In this paper, we use coordinate descent (CD) method to solve the problem. First, we show that the <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means minimization problem can be reformulated as a trace maximization problem, then a simple and efficient coordinate descent scheme is proposed to solve the maximization problem. Two interesting findings through theory are that Lloyd cannot decrease the objective function value of <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means produced by our CD further, and our proposed method CD to solve <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means problem can avoid produce empty clusters. In addition, according to the computational complexity analysis, it is verified CD has the same time complexity with original <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means method. Extensive experiments including statistical hypothesis testing, on several real-world datasets with varying number of clusters, varying number of samples and varying number of dimensions show that CD performs better compared to Lloyd, i.e., lower objective value, better local minimum and fewer iterations. And CD is more robust to initialization than Lloyd whether the initialization strategy is random or initialization of <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means++.",
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"title": "Coordinate Descent Method for <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-means",
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"abstract": "Nonlinear function calculation is widely used in numerous science and technology fields. Stochastic computation is a novel high-efficiency value representation and calculation scheme, which is helpful to reduce hardware cost. However, the main challenges of stochastic computation based nonlinear function implementation lie on poor generalization, low accuracy and high latency. In this paper, we propose a multiple driving and multiple dimension finite state machine (MM-FSM) to realize major single variable nonlinear functions used in information and signal processing areas on common platforms with low complexity and latency. We provide corresponding synthesis method of the activation parameters and conditional parameters of MM-FSM. In order to improve the calculation accuracy, we further propose an adaptive scaling algorithm for MM-FSM. The most salient feature of MM-FSM is that we can configure different types of nonlinear functions with the same MM-FSM structure. Thus, MM-FSM can be used in a wide range of stochastic based applications. Compared with the traditional stochastic scheme and Coordinate Rotation Digital Computer (CORDIC) algorithm, simulation results show that the proposed MM-FSM nonlinear function generator has significantly lower complexity while guaranteeing the calculation accuracy.",
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"content": "Nonlinear function calculation is widely used in numerous science and technology fields. Stochastic computation is a novel high-efficiency value representation and calculation scheme, which is helpful to reduce hardware cost. However, the main challenges of stochastic computation based nonlinear function implementation lie on poor generalization, low accuracy and high latency. In this paper, we propose a multiple driving and multiple dimension finite state machine (MM-FSM) to realize major single variable nonlinear functions used in information and signal processing areas on common platforms with low complexity and latency. We provide corresponding synthesis method of the activation parameters and conditional parameters of MM-FSM. In order to improve the calculation accuracy, we further propose an adaptive scaling algorithm for MM-FSM. The most salient feature of MM-FSM is that we can configure different types of nonlinear functions with the same MM-FSM structure. Thus, MM-FSM can be used in a wide range of stochastic based applications. Compared with the traditional stochastic scheme and Coordinate Rotation Digital Computer (CORDIC) algorithm, simulation results show that the proposed MM-FSM nonlinear function generator has significantly lower complexity while guaranteeing the calculation accuracy.",
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