Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2018
Abstract
Most existing subspace clustering methods hinge on self-expression of handcrafted representations and are unaware of potential clustering errors. Thus they perform unsatisfactorily on real data with complex underlying subspaces. To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering. Specifically, DASC consists of a subspace clustering generator and a quality-verifying discriminator, which learn against each other. The generator produces subspace estimation and sample clustering. The discriminator evaluates current clustering performance by inspecting whether the re-sampled data from estimated subspaces have consistent subspace properties, and supervises the generator to progressively improve subspace clustering. Experimental results on the handwritten recognition, face and object clustering tasks demonstrate the advantages of DASC over shallow and few deep subspace clustering models. Moreover, to our best knowledge, this is the first successful application of GAN-alike model for unsupervised subspace clustering, which also paves the way for deep learning to solve other unsupervised learning problems.
Keywords
Generators, Clustering methods, Fasteners, Task analysis, Feeds, Estimation
Discipline
Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23
First Page
1596
Last Page
1604
ISBN
9781538664216
Identifier
10.1109/CVPR.2018.00172
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
ZHOU, Pan; HOU, Yunqing; and FENG, Jiashi.
Deep adversarial subspace clustering. (2018). Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23. 1596-1604.
Available at: https://ink.library.smu.edu.sg/sis_research/9001
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CVPR.2018.00172