Deep Spectral Clustering Using Dual Autoencoder Network
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we...
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| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 4061 - 4070 |
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01.06.2019
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| ISSN: | 1063-6919 |
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| Abstract | The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering. As such the learned latent representations can be more robust to noise. Then the mutual information estimation is utilized to provide more discriminative information from the inputs. Furthermore, a deep spectral clustering method is applied to embed the latent representations into the eigenspace and subsequently clusters them, which can fully exploit the relationship between inputs to achieve optimal clustering results. Experimental results on benchmark datasets show that our method can significantly outperform state-of-the-art clustering approaches. |
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| AbstractList | The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering. As such the learned latent representations can be more robust to noise. Then the mutual information estimation is utilized to provide more discriminative information from the inputs. Furthermore, a deep spectral clustering method is applied to embed the latent representations into the eigenspace and subsequently clusters them, which can fully exploit the relationship between inputs to achieve optimal clustering results. Experimental results on benchmark datasets show that our method can significantly outperform state-of-the-art clustering approaches. |
| Author | Yang, Xu Yan, Junchi Deng, Cheng Zheng, Feng Liu, Wei |
| Author_xml | – sequence: 1 givenname: Xu surname: Yang fullname: Yang, Xu organization: Xidian Univ – sequence: 2 givenname: Cheng surname: Deng fullname: Deng, Cheng organization: Xidian Univ – sequence: 3 givenname: Feng surname: Zheng fullname: Zheng, Feng organization: Southern Univ. of Science and Technology – sequence: 4 givenname: Junchi surname: Yan fullname: Yan, Junchi organization: Shanghai Jiao Tong Univ – sequence: 5 givenname: Wei surname: Liu fullname: Liu, Wei organization: Tencent |
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| Snippet | The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to... |
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| SubjectTerms | Autoencoders Benchmark testing Clustering methods Computer vision Estimation Mutual information Noise Noise measurement Representation Learning Statistical Learning |
| Title | Deep Spectral Clustering Using Dual Autoencoder Network |
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