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Article Dans Une Revue (Article De Synthèse) IEEE Transactions on Knowledge and Data Engineering Année : 2024

Feature Space Recovery for Efficient Incomplete Multi-view Clustering

Résumé

T-SVD based incomplete multi-view clustering (IMVC) has received wide attention due to its ability to capture high-order correlations. However, t-SVD suffers from rotation sensitivity, failing to fully explore both inter-and intra-view consistencies. Besides, current methods mainly consider inter-or intra-view correlations, ignoring the low-rank information of sample features within views. To address these weaknesses, we first propose a feature space recovery based IMVC (FSR-IMVC) method, where low-rank feature space recovery and low-rank tensor ring based consistency learning are considered into a unified framework. Furthermore, we extend FSR-IMVC by incorporating anchor learning on the latent feature space, resulting in a scalable FSR-IMVC (sFSR-IMVC) approach that is well-suited to large-scale data. In an iterative way, the learned inter-and intra-view correlations will guide the recovery of missing features, while the explored low-rank information from feature spaces will in turn facilitate consistency exploration, eventually achieving outstanding clustering performance. Experimental results show that FSR-IMVC provides a significant improvement over known state-of-the-art algorithms in terms of ACC, NMI and Purity. Compared with FSR-IMVC, sFSR-IMVC performs slightly worse in clustering accuracy, but offers a notable advantage in computational efficiency, particularly for large-scale datasets. The codes of FSR-IMVC and sFSR-IMVC are publicly available at https://github.com/longzhen520/sFSR-IMVC.
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Dates et versions

hal-04204714 , version 1 (12-09-2023)

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Zhen Long, Ce Zhu, Pierre Comon, Yazhou Ren, Yipeng Liu. Feature Space Recovery for Efficient Incomplete Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, inPress, pp.1-14. ⟨10.1109/TKDE.2023.3333522⟩. ⟨hal-04204714⟩
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