Unsupervised feature selection via joint local learning and group sparse regression

Frontiers of Information Technology & Electronic Engineering, pp. 538-553, 2019.

Cited by: 0|Bibtex|Views37|DOI:https://doi.org/10.1631/FITEE.1700804
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Other Links: dblp.uni-trier.de|academic.microsoft.com|link.springer.com

Abstract:

Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsu...More

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