@inproceedings{ce69bd2aa61d4d28a78f2fe0081f0707,
title = "Pattern classification using eigenspace projection",
abstract = "Covariance matrices play the key role for dimension reduction in eigenspace projection methods for pattern recognition. Two scatters, an intraclass scatter and an interclass scatter, are obtained from samples for describing the sample distributions. The representation for these two scatters is classified into four categories. In this study, we focus on the analysis of the intraclass and interclass scatters. Three experiments, the evaluation for a music genre dataset, a bird sound dataset, and four face datasets, are conducted to make the comparisons of several state-of-the-art algorithms.",
keywords = "Covariance matrix, global mean-based scatter, local mean-based scatter, pairwise point-based scatter, point-to-space based scatter",
author = "Hsieh, {Chen Ta} and Han, {Chin Chuan} and Lee, {Chang Hsing} and Fan, {Kou Chin}",
year = "2012",
doi = "10.1109/IIH-MSP.2012.43",
language = "???core.languages.en_GB???",
isbn = "9780769547121",
series = "Proceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012",
pages = "154--157",
booktitle = "Proceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012",
note = "2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012 ; Conference date: 18-07-2012 Through 20-07-2012",
}