Eigenvalue Criterion-Based Feature Selectionin Principal Component Analysis of Speech

This article presents a specific approach for selecting a limited set of most relevant, information rich speech data from the whole amount of training data. The proposed method uses Principal Component Analysis (PCA) to optimally select a lower-dimensional data subset with similar variances. In this...

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Principais autores:	Peter Viszlay, Jozef Janecko, Jozef Juhar
格式:	Artigo
語言:	Inglês
出版:	VSB-Technical University of Ostrava 2012-01-01
叢編:	Advances in Electrical and Electronic Engineering
主題:	eigenvalue feature vector principal components selection criterion variance.
在線閱讀:	http://advances.utc.sk/index.php/AEEE/article/view/723
標簽:	添加標簽 沒有標簽, 成為第一個標記此記錄!