Presentation | 1999/1/21 Probabilistic Mixture Subspace Method : Pattern Recognition by Mixture of Factor Analyzers Naonori UEDA, Ryohei NAKANO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We present a classification method using mextures of factor analyzers. In this method class probability for each class is estimated from given training data in the same class by approximating the feature space with the mixture of affine subspaces with less dimensionality. The proposed method is similar to the conventional subspace methods in the sense that the classification is done based on the dimensionality reduction of the feature space. However, it is essentially different from them in that since it is formulated as a probabilistic model, it can compute posterior probabilities for unknown data. That is, the proposed method can perform Bayes classification. We apply the proposed method to real data sets and show that it outperforms the conventional subspace method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pattern recognition / Mixtures of factor analyzers / Subspace method / Bayes classification |
Paper # | NC98-72 |
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Committee | NC |
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Conference Date | 1999/1/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Probabilistic Mixture Subspace Method : Pattern Recognition by Mixture of Factor Analyzers |
Sub Title (in English) | |
Keyword(1) | Pattern recognition |
Keyword(2) | Mixtures of factor analyzers |
Keyword(3) | Subspace method |
Keyword(4) | Bayes classification |
1st Author's Name | Naonori UEDA |
1st Author's Affiliation | NTT Communication Science Laboratories() |
2nd Author's Name | Ryohei NAKANO |
2nd Author's Affiliation | NTT Communication Science Laboratories |
Date | 1999/1/21 |
Paper # | NC98-72 |
Volume (vol) | vol.98 |
Number (no) | 526 |
Page | pp.pp.- |
#Pages | 7 |
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