Proceedings of the AAAI Conference on Artificial Intelligence· 2020
Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering
- 212citations
- 2020year
Short summary
A novel weighted tensor-nuclear norm minimization (WTNNM) method based on tensor-Singular Value Decomposition (t-SVD) significantly improves multi-view subspace clustering by addressing noise and illumination variations.
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Key points
- Introduces a weighted tensor-nuclear norm minimization (WTNNM) to improve t-SVD based multi-view clustering.
- WTNNM addresses limitations of standard t-SVD in handling noise and illumination changes by weighting singular values.
- The method exploits high-order correlations within multi-view data.
- Experimental results show WTNNM outperforms existing state-of-the-art multi-view subspace clustering techniques.
AI-generated from the title and abstract; the full text is not read.
Abstract
Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD, which does not make sense in the existence of noise and illumination change. To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. We further apply the WTNNM algorithm to multi-view subspace clustering by exploiting the high order correlations embedded in different views. Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance.
The authors' abstract, as published at the source. Proceedings of the AAAI Conference on Artificial Intelligence, 2020 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics