Adaptive Attribute and Structure Subspace Clustering Network

doi:10.1109/TIP.2022.3171421

Adaptive Attribute and Structure Subspace Clustering Network

2022-01-01

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AI

DOI：10.1109/TIP.2022.3171421

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Deep self-expressiveness-based subspace clustering methods have demonstrated effectiveness. However, existing works only consider the attribute information to conduct the self-expressiveness, limiting 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 is publicly available at https://github.com/ZhihaoPENG-CityU/AASSC-Net.

Keywords:

Feature extraction

Decoding

Kernel

Data mining

Adaptive systems

Symmetric matrices

Sparse matrices

Deep learning

subspace clustering

self-expressiveness

structure information

Adaptive Attribute and Structure Subspace Clustering Network

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