On the stochastic gradient descent matrix factorization in application to the supervised classification of microarrays
Microarray datasets are highly dimensional, with a small number of collected samples in comparison to thousands of features. This poses a significant challenge that affects the interpretation, applicability and validation of the analytical results. Matrix factorizations have proven to be a useful me...
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| Format: | Article |
| Language: | Russian |
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Institute of Computer Science
2013-04-01
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| Series: | Компьютерные исследования и моделирование |
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| Online Access: | http://crm.ics.org.ru/uploads/crmissues/crm_2013_2/13202.pdf |