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Self-Weighted Clustering With Adaptive Neighbors.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Sep2020, Vol. 31 Issue 9, p3428-3441. 14p. - Publication Year :
- 2020
-
Abstract
- Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption that the importance of different features is equal, which is not adapted in practical applications. To alleviate this problem, this article proposes a self-weighted clustering with adaptive neighbors (SWCAN) model that can assign weights for different features, learn an SG, and partition samples into clusters simultaneously. In experiments, we observe that the SWCAN can assign weights for different features reasonably and outperform than comparison clustering models on synthetic and practical data sets. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CLUSTER sampling
*SUPPORT vector machines
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 31
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 145476416
- Full Text :
- https://doi.org/10.1109/TNNLS.2019.2944565