A discriminative and heteroscedastic linear feature transformation for multiclass classification

Hung Shin Lee, Hsin Min Wang, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

This paper presents a novel discriminative feature transformation, named full-rank generalized likelihood ratio discriminant analysis (fGLRDA), on the grounds of the likelihood ratio test (LRT). fGLRDA attempts to seek a feature space, which is linearly isomorphic to the original n-dimensional feature space and is characterized by a full-rank ) (nxn) transformation matrix, under the assumption that all the class-discrimination information resides in a d-dimensional subspace ) (d < n), through making the most confusing situation, described by the null hypothesis, as unlikely as possible to happen without the homoscedastic assumption on class distributions. Our experimental results demonstrate that fGLRDA can yield moderate performance improvements over other existing methods, such as linear discriminant analysis (LDA) for the speaker identification task.

原文英語
主出版物標題Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
頁面690-693
頁數4
DOIs
出版狀態已發佈 - 2010 十一月 18
事件2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, 土耳其
持續時間: 2010 八月 232010 八月 26

出版系列

名字Proceedings - International Conference on Pattern Recognition
ISSN(列印)1051-4651

其他

其他2010 20th International Conference on Pattern Recognition, ICPR 2010
國家/地區土耳其
城市Istanbul
期間2010/08/232010/08/26

ASJC Scopus subject areas

  • 電腦視覺和模式識別

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