Kernelized evolutionary distance metric learning for semi-supervised clustering
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Summary
The drawback of EDML in non-linearly separable input space is empirically demonstrated and the benefit of kernel function to extension K-EDML method is illustrated by showing its superior result benefits to other clustering algorithms in the semi-supervised clustering on various real-world datasets.
- Type
- article
- Published
- 2017-02-12
- Cited by
- 20
- References
- 63
- Access
- Open access
- OpenAlex
- https://openalex.org/W2605207659
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:29155671
Keywords
Kernelization, Mahalanobis distance, Cluster analysis, Metric (unit), Mathematics
References
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