Abstract #301941

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2002 Program page
JSM 2002 Abstract #301941
Activity Number: 197
Type: Topic Contributed
Date/Time: Tuesday, August 13, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing*
Abstract - #301941
Title: A Null Space Algorithm for Overcomplete Independent Component Analysis
Author(s): Ray Bing Chen*+ and Yingnian Wu
Affiliation(s): University of California, Los Angeles and University of California, Los Angeles
Address: , , , ,
Keywords: MCMC ; Bayesian method ; Overcomplete ICA ; linear model
Abstract:

In this work, we consider the overcomplete independent component analysis, the generalization of PCA and ICA. Even though Lewicki and Sejnowski (2000) gave an unsupervised learning algorithm for this problem, there still exist some computational difficulties in their algorithm for the zero (or small) noise. In order to avoid those difficulties, we propose a new algorithm for learning the bases vectors for the observations, and recovering the original sources from the sparse prior assumption. Here, we demonstrate that this new algorithm works for the blind separation of the speech data. The case of our experiment involve two sequences of observations mixed by three independent sources.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2002 program

JSM 2002

For information, contact meetings@amstat.org or phone (703) 684-1221.

If you have questions about the Continuing Education program, please contact the Education Department.

Revised March 2002