Structured Feature Selection

Abstract

Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery, that improves the efficiency of state-of-the-art algorithms. Then we theoretically justify three advantages of structured feature selection over traditional feature selection methods. Specifically, we show that the Markov Blanket is the minimum feature set that retains the maximal mutual information and also gives the lowest Bayes classification error. Then we apply structured feature selection to two applications: 1) We introduce a new method that enables STMB to scale up and show the competitive performance of our algorithms on large-scale image classification tasks. 2) We propose a method for structured feature selection to handle hierarchical features and show the proposed method can lead to big performance gain in facial expression and action unit (AU) recognition tasks.