Spectral nonlinearly embedded clustering algorithm
type: Item
instance of: scholarly article
title: Spectral nonlinearly embedded clustering algorithm
zbMATH Open document ID: 1400.68177
DOI: 10.1155/2016/9264561
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: As is well known, traditional spectral clustering (SC) methods are developed based on the \textit{manifold assumption}, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. But, for some high-dimensional and sparse data, such an assumption might be invalid. Consequently, the clustering performance of SC will be degraded sharply in this case. To solve this problem, in this paper, we propose a general spectral embedded framework, which embeds the true cluster assignment matrix for high-dimensional data into a nonlinear space by a predefined embedding function. Based on this framework, several algorithms are presented by using different embedding functions, which aim at learning the final cluster assignment matrix and a transformation into a low dimensionality space simultaneously. More importantly, the proposed method can naturally handle the out-of-sample extension problem. The experimental results on benchmark datasets demonstrate that the proposed method significantly outperforms existing clustering methods.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6953764
Wikidata QID: Q59141045
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/9264561
OpenAlex ID: W2461263235
cites work: A survey of kernel and spectral methods for clustering
cites work: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
cites work: Q3174044
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description: scientific article; zbMATH DE number 6953764