Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kullback-Leibler divergence, we use our proposed criterion to filter out redundant features for the purpose of multiclass classification. This approach results in an efficient and fast non-parametric implementation of feature selection as it can be directly estimated using a geometric measure of dependency, the global Friedman-Rafsky (FR) multivariate run test statistic constructed by a global minimal spanning tree (MST). We demonstrate the advantages of our proposed feature selection approach through simulation. In addition the proposed feature selection method is applied to the MNIST data set.
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
University of Rochester（罗切斯特大学）；Baylor College of Medicine（贝勒医学院）；University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）；University of Washington（华盛顿大学）
Dependency-aware Maximum Likelihood Estimation for Active Learning
Department of Electrical and Computer Engineering（电气与计算机工程系）；Northeastern University（东北大学）；Department of Computer Science（计算机科学系）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
