Faster streaming algorithms for low-rank matrix approximations
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 53-55). === Low-rank matrix approximations are used in a significant number of application...
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| Format: | Others |
| Language: | English |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/91810 |
| Summary: | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 53-55). === Low-rank matrix approximations are used in a significant number of applications. We present new algorithms for generating such approximations in a streaming fashion that expand upon recently discovered matrix sketching techniques. We test our approaches on real and synthetic data to explore runtime and accuracy performance. We apply our algorithms to the technique of Latent Semantic Indexing on a widely studied data set. We find our algorithms provide strong empirical results. === by Timothy Matthew Galvin. === M. Eng. |
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