Locality Relationship Constrained Multi-view Clustering Framework
- URL: http://arxiv.org/abs/2107.05073v1
- Date: Sun, 11 Jul 2021 15:45:10 GMT
- Title: Locality Relationship Constrained Multi-view Clustering Framework
- Authors: Xiangzhu Meng, Wei Wei, Wenzhe Liu
- Abstract summary: Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF) is presented.
It aims to explore the diversity, geometric, consensus and complementary information among different views.
LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure.
- Score: 5.586948325488168
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In most practical applications, it's common to utilize multiple features from
different views to represent one object. Among these works, multi-view
subspace-based clustering has gained extensive attention from many researchers,
which aims to provide clustering solutions to multi-view data. However, most
existing methods fail to take full use of the locality geometric structure and
similarity relationship among samples under the multi-view scenario. To solve
these issues, we propose a novel multi-view learning method with locality
relationship constraint to explore the problem of multi-view clustering, called
Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF).
LRC-MCF aims to explore the diversity, geometric, consensus and complementary
information among different views, by capturing the locality relationship
information and the common similarity relationships among multiple views.
Moreover, LRC-MCF takes sufficient consideration to weights of different views
in finding the common-view locality structure and straightforwardly produce the
final clusters. To effectually reduce the redundancy of the learned
representations, the low-rank constraint on the common similarity matrix is
considered additionally. To solve the minimization problem of LRC-MCF, an
Alternating Direction Minimization (ADM) method is provided to iteratively
calculate all variables LRC-MCF. Extensive experimental results on seven
benchmark multi-view datasets validate the effectiveness of the LRC-MCF method.
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