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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| Format: | Article |
| Language: | Russian |
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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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| _version_ | 1849336273136779264 |
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| author | D. V. Pradun |
| author_facet | D. V. Pradun |
| author_sort | D. V. Pradun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-58f0807e6e4d4dc5896cd0ebed4ee741 |
| institution | Kabale University |
| issn | 1816-0301 |
| language | Russian |
| publishDate | 2016-09-01 |
| publisher | National Academy of Sciences of Belarus, the United Institute of Informatics Problems |
| record_format | Article |
| series | Informatika |
| spelling | doaj-art-58f0807e6e4d4dc5896cd0ebed4ee7412025-08-20T03:45:02ZrusNational Academy of Sciences of Belarus, the United Institute of Informatics ProblemsInformatika1816-03012016-09-0101576535REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSISD. V. Pradun0Объединенный институт проблем информатики НАН БеларусиThe 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.https://inf.grid.by/jour/article/view/36 |
| spellingShingle | D. V. Pradun REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS Informatika |
| title | REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS |
| title_full | REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS |
| title_fullStr | REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS |
| title_full_unstemmed | REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS |
| title_short | REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS |
| title_sort | reduction of training samples dimension in pattern recognition of space images using principal components analysis |
| url | https://inf.grid.by/jour/article/view/36 |
| work_keys_str_mv | AT dvpradun reductionoftrainingsamplesdimensioninpatternrecognitionofspaceimagesusingprincipalcomponentsanalysis |