Flexible Feature Spaces Based on Generalized Heteroscedastic Linear Discriminant Analysis

Author

Duminuco, Alessandro ; Liu, Chaojun ; Kryze, David ; Rigazio, Luca

Author_Institution

Panasonic Digital Networking Lab., Santa Barbara, CA

Volume

1

fYear

2006

fDate

14-19 May 2006

Abstract

This paper presents a generalized feature projection scheme which allows each feature dimension to be classified in a set of 1 to M classes, where M is the total number of classes. Our method is an extension of the classical full-space null-space approach where each dimension can only be classified in either M classes or 1 class. We believe that this more general formulation allows for a better trade-off of number of parameters versus model complexity, which in turn should provide better classification. We first tested GLDA on TIMIT and obtained an improvement up to 1% in phone classification rate over the best HLDA classifier. Preliminary results on Wall Street Journal 20K also show an improvement over the best HLDA system of about 0.2% absolute

Keywords

feature extraction; matrix algebra; maximum likelihood estimation; pattern classification; flexible feature spaces; generalized feature projection scheme; generalized heteroscedastic linear discriminant analysis; maximum likelihood estimation; phone classification rate; transformation matrix; Chaos; Covariance matrix; Laboratories; Linear discriminant analysis; Maximum likelihood estimation; Pattern classification; Testing; Tree graphs; Unsolicited electronic mail; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660021

Filename

1660021