REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS
The essence of principal components analysis and the problem of dimension reduction are described. A method of principal components calculation is presented, which is based on the covariance matrix eigenvalues determination. Practical implementations of principal components analysis are described, w...
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| Main Author: | |
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| Format: | Article |
| Language: | Russian |
| Published: |
National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2016-09-01
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| Series: | Informatika |
| Online Access: | https://inf.grid.by/jour/article/view/36 |
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| Summary: | The essence of principal components analysis and the problem of dimension reduction are described. A method of principal components calculation is presented, which is based on the covariance matrix eigenvalues determination. Practical implementations of principal components analysis are described, which are based on QR-algorithm. Application of principal components analysis in space images classification for the reduction of training samples dimension is discussed. |
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| ISSN: | 1816-0301 |