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Abstract: Motivated by authentication, intrusion and spam detection applications we consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the learner has a sample from a target distribution and the goal is to construct a classifier capable of distinguishing o... |
Title: Reading Dependencies from Covariance Graphs |
Abstract: The covariance graph (aka bi-directed graph) of a probability distribution $p$ is the undirected graph $G$ where two nodes are adjacent iff their corresponding random variables are marginally dependent in $p$. In this paper, we present a graphical criterion for reading dependencies from $G$, under the assumpt... |
Title: Uniform Approximation of Vapnik-Chervonenkis Classes |
Abstract: For any family of measurable sets in a probability space, we show that either (i) the family has infinite Vapnik-Chervonenkis (VC) dimension or (ii) for every epsilon > 0 there is a finite partition pi such the pi-boundary of each set has measure at most epsilon. Immediate corollaries include the fact that a ... |
Title: New S-norm and T-norm Operators for Active Learning Method |
Abstract: Active Learning Method (ALM) is a soft computing method used for modeling and control based on fuzzy logic. All operators defined for fuzzy sets must serve as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling method, ALM does not possess operators which serve as S-norms and T-norms which ... |
Title: A Partial Taxonomy of Substitutability and Interchangeability |
Abstract: Substitutability, interchangeability and related concepts in Constraint Programming were introduced approximately twenty years ago and have given rise to considerable subsequent research. We survey this work, classify, and relate the different concepts, and indicate directions for future work, in particular w... |
Title: Introduction to the Special Issue: Genome-Wide Association Studies |
Abstract: Introduction to the Special Issue: Genome-Wide Association Studies |
Title: The Role of Family-Based Designs in Genome-Wide Association Studies |
Abstract: Genome-Wide Association Studies (GWAS) offer an exciting and promising new research avenue for finding genes for complex diseases. Traditional case-control and cohort studies offer many advantages for such designs. Family-based association designs have long been attractive for their robustness properties, but... |
Title: Genome-Wide Significance Levels and Weighted Hypothesis Testing |
Abstract: Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered usi... |
Title: Methodological Issues in Multistage Genome-Wide Association Studies |
Abstract: Because of the high cost of commercial genotyping chip technologies, many investigations have used a two-stage design for genome-wide association studies, using part of the sample for an initial discovery of ``promising'' SNPs at a less stringent significance level and the remainder in a joint analysis of jus... |
Title: A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies |
Abstract: The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We ... |
Title: Population Structure and Cryptic Relatedness in Genetic Association Studies |
Abstract: We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple ``island'' model of population structure. We take a broader approach, which views population structure an... |
Title: Structures and Assumptions: Strategies to Harness Gene $\times$ Gene and Gene $\times$ Environment Interactions in GWAS |
Abstract: Genome-wide association studies, in which as many as a million single nucleotide polymorphisms (SNP) are measured on several thousand samples, are quickly becoming a common type of study for identifying genetic factors associated with many phenotypes. There is a strong assumption that interactions between SNP... |
Title: Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes |
Abstract: Although prospective logistic regression is the standard method of analysis for case-control data, it has been recently noted that in genetic epidemiologic studies one can use the ``retrospective'' likelihood to gain major power by incorporating various population genetics model assumptions such as Hardy-Wein... |
Title: Estimating Effects and Making Predictions from Genome-Wide Marker Data |
Abstract: In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called ``winner's curse.'' We argue that the classical definition of u... |
Title: Learning under Concept Drift: an Overview |
Abstract: Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and term... |
Title: A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models |
Abstract: We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian Markov random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting model, which we call maximum entropy ... |
Title: Collaborative Sources Identification in Mixed Signals via Hierarchical Sparse Modeling |
Abstract: A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dict... |
Title: f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models |
Abstract: A density ratio is defined by the ratio of two probability densities. We study the inference problem of density ratios and apply a semi-parametric density-ratio estimator to the two-sample homogeneity test. In the proposed test procedure, the f-divergence between two probability densities is estimated using a... |
Title: Local Component Analysis for Nonparametric Bayes Classifier |
Abstract: The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on n... |
Title: Using GWAS Data to Identify Copy Number Variants Contributing to Common Complex Diseases |
Abstract: Copy number variants (CNVs) account for more polymorphic base pairs in the human genome than do single nucleotide polymorphisms (SNPs). CNVs encompass genes as well as noncoding DNA, making these polymorphisms good candidates for functional variation. Consequently, most modern genome-wide association studies ... |
Title: On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs |
Abstract: Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies. We compared several procedures to combine GWA study data both in terms ... |
Title: Robust Tests in Genome-Wide Scans under Incomplete Linkage Disequilibrium |
Abstract: Under complete linkage disequilibrium (LD), robust tests often have greater power than Pearson's chi-square test and trend tests for the analysis of case-control genetic association studies. Robust statistics have been used in candidate-gene and genome-wide association studies (GWAS) when the genetic model is... |
Title: Replication in Genome-Wide Association Studies |
Abstract: Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication, issues of heterogeneity, advantages and disa... |
Title: Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors |
Abstract: This paper examines historical patterns of ROA (return on assets) for a cohort of 53,038 publicly traded firms across 93 countries, measured over the past 45 years. Our goal is to screen for firms whose ROA trajectories suggest that they have systematically outperformed their peer groups over time. Such a pro... |
Title: Regularization for Cox's proportional hazards model with NP-dimensionality |
Abstract: High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of nonconcave penalized methods for nonpol... |
Title: Parameter expansion in local-shrinkage models |
Abstract: This paper considers the problem of using MCMC to fit sparse Bayesian models based on normal scale-mixture priors. Examples of this framework include the Bayesian LASSO and the horseshoe prior. We study the usefulness of parameter expansion (PX) for improving convergence in such models, which is notoriously s... |
Title: Converged Algorithms for Orthogonal Nonnegative Matrix Factorizations |
Abstract: This paper proposes uni-orthogonal and bi-orthogonal nonnegative matrix factorization algorithms with robust convergence proofs. We design the algorithms based on the work of Lee and Seung [1], and derive the converged versions by utilizing ideas from the work of Lin [2]. The experimental results confirm the ... |
Title: Random threshold for linear model selection, revisited |
Abstract: In [Lavielle and Ludena 07], a random thresholding metho d is intro duced to select the significant, or non null, mean terms among a collection of independent random variables, and applied to the problem of recovering the significant coefficients in non ordered model selection. We intro duce a simple modifica... |
Title: On optimizing over lift-and-project closures |
Abstract: The lift-and-project closure is the relaxation obtained by computing all lift-and-project cuts from the initial formulation of a mixed integer linear program or equivalently by computing all mixed integer Gomory cuts read from all tableau's corresponding to feasible and infeasible bases. In this paper, we pre... |
Title: Translation-Invariant Representation for Cumulative Foot Pressure Images |
Abstract: Human can be distinguished by different limb movements and unique ground reaction force. Cumulative foot pressure image is a 2-D cumulative ground reaction force during one gait cycle. Although it contains pressure spatial distribution information and pressure temporal distribution information, it suffers fro... |
Title: Resource-bounded Dimension in Computational Learning Theory |
Abstract: This paper focuses on the relation between computational learning theory and resource-bounded dimension. We intend to establish close connections between the learnability/nonlearnability of a concept class and its corresponding size in terms of effective dimension, which will allow the use of powerful dimensi... |
Title: Theory of spike timing based neural classifiers |
Abstract: We study the computational capacity of a model neuron, the Tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast to its static analog, the Perceptron... |
Title: A GMBCG Galaxy Cluster Catalog of 55,424 Rich Clusters from SDSS DR7 |
Abstract: We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique fo... |
Title: Efficient Minimization of Decomposable Submodular Functions |
Abstract: Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of convex functions, and can be minimized in strongly polynomial time. Unfortunately, state-of-the-art algorithms for general submodular... |
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