Singular value decomposition and data compression techniques
This project addresses computational methods for obtaining singular values and singular vectors of matrices, focusing on the large-scale setting. Strategies of data compression based on the statistical technique of principal components analysis are our main motivation. At first, Lanczos method, whic...
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| Main Authors: | , |
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| Format: | Online |
| Language: | Portuguese |
| Published: |
Universidade Estadual de Campinas
2019
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| Subjects: | |
| Online Access: | https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/1599 |
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| Summary: | This project addresses computational methods for obtaining singular values and singular vectors of matrices, focusing on the large-scale setting. Strategies of data compression based on the statistical technique of principal components analysis are our main motivation. At first, Lanczos method, which is a matrix-free strategy to determine a set of eigenpairs of symmetric matrices, was studied and implemented. Then, such fundamentals methods were used to obtain a partial singular value decomposition of data matrices, in order to explore practical problems by means of the principal components analysis. In particular, experimental results were perfomed in image compression. |
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| ISSN: | 2596-1969 |
