Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion

The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a novel perspective revealing the two key factors in information utilization: class-relevance and redundancy. We derive a new information decomposition model where a novel concept called class-relevant redundancy is introduced. Subsequently a new algorithm called Conditional Informative Feature Extraction is formulated, which maximizes the joint class-relevant information by explicitly reducing the class-relevant redundancies among features. To address the computational difficulties in information-based optimization, we incorporate Parzen window estimation into the discrete approximation of the objective function and propose a Local Active Region method which substantially increases the optimization efficiency. To effectively utilize the extracted feature set, we propose a Bayesian MAP formulation for feature fu...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Dahua Lin, Xiaoou Tang
• Keywords: Computer Vision, ECCV 2006, Extraction Stage, Feature Fusion, Fusion Stage, Informative Feature Extraction, Parzen Window Estimation