ICA and ISA using Schweizer-Wolff measure of dependence

We propose a new algorithm for independent component and independent subspace analysis problems. This algorithm uses a contrast based on the Schweizer-Wolff measure of pairwise dependence (Schweizer & Wolff, 1981), a non-parametric measure computed on pairwise ranks of the variables. Our algorithm frequently outperforms state of the art ICA methods in the normal setting, is significantly more robust to outliers in the mixed signals, and performs well even in the presence of noise. Our method can also be used to solve independent subspace analysis (ISA) problems by grouping signals recovered by ICA methods. We provide an extensive empirical evaluation using simulated, sound, and image data.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Barnabás Póczos, Sergey Kirshner
• Keywords: Art Ica Methods, ICML 2008, Independent Subspace Analysis, Machine Learning, Subspace Analysis Problems