Automatically assigned DDC number: 00

Manually assigned DDC number: 006312

Number of references: 0

Title: ICA Mixture Models for Unsupervised Classification of Non-Gaussian Sources and Automatic Context Switching in Blind Signal Separation

Author:

Author:

Author:

Subject: Te-won Lee,Michael S. Lewicki,Terrence J. Sejnowski ICA Mixture Models for Unsupervised Classification of Non-Gaussian Sources and Automatic Context Switching in Blind Signal Separation

Description: An unsupervised classification algorithm is derived from an ICA mixture model assuming that the observed data can be categorized into several mutually exclusive data classes whose components are generated by linear mixtures of independent non-Gaussian sources. The algorithm finds the independent sources, the mixing matrix for each class and also computes the class membership probability for each data point. The new algorithm can improve classification accuracy compared with standard Gaussian mixture models. When applied to blind source separation in nonstationary environments, the method can switch automatically between learned mixing matrices. The algorithm can learn efficient codes to represent images containing both natural scenes and text. This method shows promise for modeling structure in high-dimensional data and has many potential applications. Index Terms: Unsupervised classification, Gaussian mixture model, independent component analysis, blind source separation, image coding...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-03-02

Pubyear: 0

Format: ps

Identifier: http://citeseer.ist.psu.edu/140902.html

Source: http://www.cnl.salk.edu/~tewon/Blind/WWW/Public/pami99.ps.gz

Language: en

Rights: unrestricted