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2016 | OriginalPaper | Buchkapitel

Learning Latent Features for Multi-view Clustering Based on NMF

verfasst von : Mengjiao He, Yan Yang, Hongjun Wang

Erschienen in: Rough Sets

Verlag: Springer International Publishing

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Abstract

Multi-view data coming from multiple ways or being presented in multiple forms, have more information than single-view data. So multi-view clustering benefits from exploiting the more information. Nonnegative matrix factorization (NMF) is an efficient method to learn low-rank approximation of nonnegative matrix of nonnegative data, but it may not be good at clustering. This paper presents a novel multi-view clustering algorithm (called MVCS) which properly combines the similarity and NMF. It aims to obtain latent features shared by multiple views with factorizations, which is a common factor matrix attained from the views and the common similarity matrix. Besides, according to the reconstruction precisions of data matrices, MVCS could adaptively learn the weight. Experiments on real-world data sets demonstrate that our approach may effectively facilitate multi-view clustering and induce superior clustering results.

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Titel: Learning Latent Features for Multi-view Clustering Based on NMF
verfasst von: Mengjiao He
Yan Yang
Hongjun Wang
Verlag: Springer International Publishing
Buch: Rough Sets
Print ISBN: 978-3-319-47159-4

Electronic ISBN: 978-3-319-47160-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-47160-0_42

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