Adaptive Attribute and Structure Subspace Clustering Network
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3430-3439 |
Number of pages | 10 |
Journal / Publication | IEEE Transactions on Image Processing |
Volume | 31 |
Online published | 5 May 2022 |
Publication status | Published - 2022 |
Link(s)
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/AASSC-Net.
Research Area(s)
- Deep learning, subspace clustering, self-expressiveness, structure information
Citation Format(s)
Adaptive Attribute and Structure Subspace Clustering Network. / Peng, Zhihao; Liu, Hui; Jia, Yuheng et al.
In: IEEE Transactions on Image Processing, Vol. 31, 2022, p. 3430-3439.
In: IEEE Transactions on Image Processing, Vol. 31, 2022, p. 3430-3439.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review