Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
- URL: http://arxiv.org/abs/2205.10481v1
- Date: Sat, 21 May 2022 01:47:17 GMT
- Title: Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
- Authors: Guanxing Lu, Yuheng Jia, Junhui Hou
- Abstract summary: We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
- Score: 64.49871502193477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this letter, we propose a novel semi-supervised subspace clustering
method, which is able to simultaneously augment the initial supervisory
information and construct a discriminative affinity matrix. By representing the
limited amount of supervisory information as a pairwise constraint matrix, we
observe that the ideal affinity matrix for clustering shares the same low-rank
structure as the ideal pairwise constraint matrix. Thus, we stack the two
matrices into a 3-D tensor, where a global low-rank constraint is imposed to
promote the affinity matrix construction and augment the initial pairwise
constraints synchronously. Besides, we use the local geometry structure of
input samples to complement the global low-rank prior to achieve better
affinity matrix learning. The proposed model is formulated as a Laplacian graph
regularized convex low-rank tensor representation problem, which is further
solved with an alternative iterative algorithm. In addition, we propose to
refine the affinity matrix with the augmented pairwise constraints.
Comprehensive experimental results on six commonly-used benchmark datasets
demonstrate the superiority of our method over state-of-the-art methods. The
code is publicly available at
https://github.com/GuanxingLu/Subspace-Clustering.
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