PCA versus LDA (2001)
| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 219 - 14 self |
BibTeX
@ARTICLE{Martinez01pcaversus,
author = {Aleix M. Martinez and Aleix M. Mart'inez and Avinash C. Kak},
title = {PCA versus LDA},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2001},
volume = {23},
pages = {228--233}
}
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Abstract
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (Linear Discriminant Analysis) are superior to those based on PCA (Principal Components Analysis) . In this communication we show that this is not always the case. We present our case first by using intuitively plausible arguments and then by showing actual results on a face database. Our overall conclusion is that when the training dataset is small, PCA can outperform LDA, and also that PCA is less sensitive to different training datasets. Keywords: face recognition, pattern recognition, principal components analysis, linear discriminant analysis, learning from undersampled distributions, small training datasets. 1
