Empirical error rate minimization based linear discriminant analysis

Hung Shin Lee, Berlin Chen

研究成果: 書貢獻/報告類型會議貢獻

8 引文 斯高帕斯(Scopus)

摘要

Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space while retaining geometrical class separability. However, LDA cannot always guarantee better classification accuracy. One of the possible reasons lies in that its formulation is not directly associated with the classification error rate, so that it is not necessarily suited for the allocation rule governed by a given classifier, such as that employed in automatic speech recognition (ASR). In this paper, we extend the classical LDA by leveraging the relationship between the empirical classification error rate and the Mahalanobis distance for each respective class pair, and modify the original between-class scatter from a measure of the squared Euclidean distance to the pairwise empirical classification accuracy for each class pair, while preserving the lightweight solvability and taking no distributional assumption, just as what LDA does. Experimental results seem to demonstrate that our approach yields moderate improvements over LDA on the large vocabulary continuous speech recognition (LVCSR) task.

原文英語
主出版物標題2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
頁面1801-1804
頁數4
DOIs
出版狀態已發佈 - 2009 九月 23
事件2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, 臺灣
持續時間: 2009 四月 192009 四月 24

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

其他

其他2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
國家臺灣
城市Taipei
期間2009/04/192009/04/24

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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