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Unsupervised Feature Selection via Adaptive Multimeasure Fusion.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Sep2019, Vol. 30 Issue 9, p2886-2892, 7p
- Publication Year :
- 2019
-
Abstract
- Since multiple criteria can be adopted to estimate the similarity among the given data points, problem regarding diverse representations of pairwise relations is brought about. To address this issue, a novel self-adaptive multimeasure (SAMM) fusion problem is proposed, such that different measure functions can be adaptively merged into a unified similarity measure. Different from other approaches, we optimize similarity as a variable instead of presetting it as a priori, such that similarity can be adaptively evaluated based on integrating various measures. To further obtain the associated subspace representation, a graph-based dimensionality reduction problem is incorporated into the proposed SAMM problem, such that the related subspace can be achieved according to the unified similarity. In addition, sparsity-inducing $\ell _{2,0}$ regularization is introduced, such that a sparse projection is obtained for efficient feature selection (FS). Consequently, the SAMM-FS method can be summarized correspondingly. [ABSTRACT FROM AUTHOR]
- Subjects :
- FEATURE selection
SIMILARITY (Geometry)
SPARSE matrices
MATHEMATICAL regularization
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 30
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 138255944
- Full Text :
- https://doi.org/10.1109/TNNLS.2018.2884487