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

    7 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09/4/1909/4/24

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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