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2018 | OriginalPaper | Buchkapitel

Multi-view Proximity Learning for Clustering

verfasst von : Kun-Yu Lin, Ling Huang, Chang-Dong Wang, Hong-Yang Chao

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based approaches. However, in this way, neither the intra-view cluster structure nor the inter-view correlation is considered in constructing proximity matrices. To address this issue, we propose a novel method, named multi-view proximity learning. By introducing the idea of representative, our model can consider both the relations between data objects and the cluster structure within individual views. Besides, the spectral-embedding-based scheme is adopted for modeling the correlations across different views, i.e. the view consistency and complement properties. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.

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The code is available on https://www.dropbox.com/s/tj5zc7yry0ing3l/MVPL_PCode.zip?dl=0 and the password for decompression is “DASFAA2018”.

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Titel: Multi-view Proximity Learning for Clustering
verfasst von: Kun-Yu Lin
Ling Huang
Chang-Dong Wang
Hong-Yang Chao
Verlag: Springer International Publishing
Buch: Database Systems for Advanced Applications
Print ISBN: 978-3-319-91457-2

Electronic ISBN: 978-3-319-91458-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91458-9_25

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