Adaptive Attribute and Structure Subspace Clustering Network
- URL: http://arxiv.org/abs/2109.13742v1
- Date: Tue, 28 Sep 2021 14:00:57 GMT
- Title: Adaptive Attribute and Structure Subspace Clustering Network
- Authors: Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou
- Abstract summary: We propose a novel self-expressiveness-based subspace clustering network.
We first consider an auto-encoder to represent input data samples.
Then, we construct a mixed signed and symmetric structure matrix to capture the local geometric structure underlying data.
We perform self-expressiveness on the constructed attribute structure and matrices to learn their affinity graphs.
- Score: 49.040136530379094
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep self-expressiveness-based subspace clustering methods have demonstrated
effectiveness. However, existing works only consider the attribute information
to conduct the self-expressiveness, which may limit the clustering performance.
In this paper, we propose a novel adaptive attribute and structure subspace
clustering network (AASSC-Net) to simultaneously consider the attribute and
structure information in an adaptive graph fusion manner. Specifically, we
first exploit an auto-encoder to represent input data samples with latent
features for the construction of an attribute matrix. We also construct a mixed
signed and symmetric structure matrix to capture the local geometric structure
underlying data samples. Then, we perform self-expressiveness on the
constructed attribute and structure matrices to learn their affinity graphs
separately. Finally, we design a novel attention-based fusion module to
adaptively leverage these two affinity graphs to construct a more
discriminative affinity graph. Extensive experimental results on commonly used
benchmark datasets demonstrate that our AASSC-Net significantly outperforms
state-of-the-art methods. In addition, we conduct comprehensive ablation
studies to discuss the effectiveness of the designed modules. The code will be
publicly available at https://github.com/ZhihaoPENG-CityU.
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