Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering

doi:10.1109/TIP.2024.3388969

Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering

2024-01-01

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DOI：10.1109/TIP.2024.3388969

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Multi-view subspace clustering (MVSC) has drawn significant attention in recent study. In this paper, we propose a novel approach to MVSC. First, the new method is capable of preserving high-order neighbor information of the data, which provides essential and complicated underlying relationships of the data that is not straightforwardly preserved by the first-order neighbors. Second, we design log-based nonconvex approximations to both tensor rank and tensor sparsity, which are effective and more accurate than the convex approximations. For the associated shrinkage problems, we provide elegant theoretical results for the closed-form solutions, for which the convergence is guaranteed by theoretical analysis. Moreover, the new approximations have some interesting properties of shrinkage effects, which are guaranteed by elegant theoretical results. Extensive experimental results confirm the effectiveness of the proposed method.

Keywords:

Multi-view

subspace clustering

low-rank

Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering

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