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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

28.05.2020

Local Structure Preservation for Nonlinear Clustering

verfasst von: Linjun Chen, Guangquan Lu, Yangding Li, Jiaye Li, Malong Tan

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

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Abstract

In this paper, we propose a new nonlinear clustering method to preserve local structure of the features. Specifically, our method applies the gaussian kernel function to achieve high dimensional projection so as to make the original data linearly separable. Our method establishes the similarity matrix of data features in low-dimensional space to conduct local structure learning, as a result, it can avoid the divergence of sample sets and retain the original nearest neighbor structural relations. Furthermore, our method uses the sparse learning to remove the redundant features to make the model more robust in the process of learning. Experimental results on eight benchmark datasets show that our proposed method was superior to the state-of-the-art clustering methods in terms of clustering performance.
Vorheriger Artikel Sparse Low-Rank and Graph Structure Learning for Supervised Feature Selection
Nächster Artikel Using Locality Preserving Projections to Improve the Performance of Kernel Clustering

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Metadaten
Titel
Local Structure Preservation for Nonlinear Clustering
verfasst von
Linjun Chen
Guangquan Lu
Yangding Li
Jiaye Li
Malong Tan
Publikationsdatum
28.05.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10251-6

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