An information-theoretic approach to unsupervised feature selection for high-dimensional data
In this paper, we model the unsupervised learning of a sequence of observed data vector as a problem of extracting joint patterns among random variables. In particular, we formulate an information-theoretic problem to extract common features of random variables by measuring the loss of total correla...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/131015 |