Cross-modal subspace clustering via deep canonical correlation analysis
文献类型：会议论文
| 作者 | Gao QX(高全学)2; Lian, Huanhuan2; Wang QQ(王倩倩)2; Sun G(孙干)1
|
| 出版日期 | 2020 |
| 会议日期 | February 7-12, 2020 |
| 会议地点 | New York |
| 页码 | 3938-3945 |
| 英文摘要 | For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods. |
| 源文献作者 | Association for the Advancement of Artificial Intelligence |
| 产权排序 | 2 |
| 会议录 | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
 |
| 会议录出版者 | AAAI press |
| 会议录出版地 | Palo Alto, CA |
| 语种 | 英语 |
| ISBN号 | 978-1-57735-835-0 |
| WOS记录号 | WOS:000667722804002 |
| 源URL | [http://ir.sia.cn/handle/173321/28935]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Lian, Huanhuan; Wang QQ(王倩倩) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 2.State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China |
| 推荐引用方式 GB/T 7714 | Gao QX,Lian, Huanhuan,Wang QQ,et al. Cross-modal subspace clustering via deep canonical correlation analysis[C]. 见:. New York. February 7-12, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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