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Neural Processing Letters

Erschienen in:

27.07.2020

Robust Subspace Clustering via Latent Smooth Representation Clustering

verfasst von: Xiaobo Xiao, Lai Wei

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

Subspace clustering aims to group high-dimensional data samples into several subspaces which they were generated. Among the existing subspace clustering methods, spectral clustering-based algorithms have attracted considerable attentions because of their predominant performances shown in many subspace clustering applications. In this paper, we proposed to apply smooth representation clustering (SMR) to the reconstruction coefficient vectors which were obtained by sparse subspace clustering (SSC). Because the reconstruction coefficient vectors could be regarded as a kind of good representations of original data samples, the proposed method could be considered as a SMR performed in a latent subspace found by SSC and hoped to achieve better performances. For solving the proposed latent smooth representation algorithm (LSMR), we presented an optimization method and also discussed the relationships between LSMR with some related algorithms. Finally, experiments conducted on several famous databases demonstrate that the proposed algorithm dominates the related algorithms.

Vorheriger Artikel A New Fixed-Time Stability Criterion and Its Application to Synchronization Control of Memristor-Based Fuzzy Inertial Neural Networks with Proportional Delay

Nächster Artikel Feature Selection Method Based on Differential Correlation Information Entropy

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In our experiments, \(\varepsilon =0.01.\)

It also contains a sequence of 5 motions which is called “dancing”. We neglect this sub-database in our experiments.

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Titel: Robust Subspace Clustering via Latent Smooth Representation Clustering
verfasst von: Xiaobo Xiao
Lai Wei
Publikationsdatum: 27.07.2020
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-020-10306-8

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