Low-Rank Tensor Regularized Fuzzy Clustering for Multiview Data
| Autor: | Huiqin Wei, Long Chen, Keyu Ruan, Lingxi Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fuzzy clustering
Kullback–Leibler divergence Computer science business.industry Augmented Lagrangian method Applied Mathematics Data classification Pattern recognition 02 engineering and technology Data modeling Euclidean distance ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Artificial intelligence Cluster analysis business |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 28:3087-3099 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2020.2988841 |
| Popis: | Since data are collected from a range of sources via different techniques, multiview clustering has become an emerging technique for unsupervised data classification. However, most existing soft multiview clustering methods only consider the pairwise correlations and ignore high-order correlations among multiple views. To integrate more comprehensive information from different views, this article innovates a fuzzy clustering model using the low-rank tensor to address the multiview data clustering problem. Our method first conducts a standard fuzzy clustering on different views of the data separately. Then, the obtained soft partition results are aggregated as the new data to be handled by a Kullback–Leibler (KL) divergence-based fuzzy model with low-rank tensor constraints. The KL divergence function, which replaces the traditional minimized Euclidean distance, can enhance the robustness of the model. More importantly, we formulate fuzzy partition matrices of different views as a third-order tensor. So, a low-rank tensor is introduced as a norm constraint in the KL divergence-based fuzzy clustering to obtain dexterously high-order correlations of different views. The minimization of the final model is convex and we present an efficient augmented Lagrangian alternating direction method to handle this problem. Specially, the global membership is derived by using tensor factorization. The efficiency and superiority of the proposed approach are demonstrated by the comparison with state-of-the-art multiview clustering algorithms on many multiple-view data sets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|