Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification

Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance. Generally, in the field of multi-view learning, these algorithms construct informative graph for each view, on which the foll...

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Bibliographic Details
Published in:	IEEE transactions on image processing 2018-03, Vol.27 (3), p.1501-1511
Main Authors:	Nie, Feiping, Cai, Guohao, Li, Jing, Li, Xuelong
Format:	Article
Language:	eng
Subjects:	Algorithms Auto-weight learning Clustering Clustering algorithms Clustering methods Correlation Datasets Image classification Kernel Laplace equations Machine learning Manifolds multi-view clustering Optimization semi-supervised classification Tensile stress
Online Access:	Get full text
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Summary:	Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance. Generally, in the field of multi-view learning, these algorithms construct informative graph for each view, on which the following clustering or classification procedure are based. However, in many real-world data sets, original data always contain noises and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without explicit weight definition and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world data sets show that the proposed model outperforms other state-of-the-art multi-view algorithms.
ISSN:	1057-7149 1941-0042