Incomplete Multi-view Clustering via Subspace Learning
文献类型：会议论文
| 作者 | Yin, Qiyue ; Wu, Shu; Wang, Liang
|
| 出版日期 | 2015 |
| 会议日期 | Oct 24-28 |
| 会议地点 | Melbourne |
| 关键词 | Multi-view Clustering Incomplete Multi-view Data Feature Selection |
| 英文摘要 | Multi-view clustering, which explores complementary information between multiple distinct feature sets for better clustering, has a wide range of applications, e.g., knowledge management and information retrieval. Traditional multiview clustering methods usually assume that all examples have complete feature sets. However, in real applications, it is often the case that some examples lose some feature sets, which results in incomplete multi-view data and notable performance degeneration. In this paper, a novel incomplete multi-view clustering method is therefore developed, which learns unified latent representations and projection matrices for the incomplete multi-view data. To approximate the high level scaled indicator matrix defined to represent class label matrix, the latent representations are expected to be non-negative and column orthogonal. Besides, since data are often with high dimensional and noisy features, the projection matrices are enforced to be sparse so as to select relevant features when learning the latent space. Furthermore, the inter-view and intra-view data structure is preserved to further enhance the clustering performance. To these ends, an objective is developed with efficient optimization strategy and convergence analysis. Extensive experiments demonstrate that our model performs better than the state-of-the-art multi-view clustering methods in various settings. |
| 会议录 | In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12334]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Wu, Shu |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Yin, Qiyue,Wu, Shu,Wang, Liang. Incomplete Multi-view Clustering via Subspace Learning[C]. 见:. Melbourne. Oct 24-28. |
入库方式： OAI收割
来源：自动化研究所
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