Data-Adaptive Transformed Bilateral Tensor Low-Rank Representation for Clustering

• URL: http://arxiv.org/abs/2510.20077v1
• Date: Wed, 22 Oct 2025 23:25:44 GMT
• Title: Data-Adaptive Transformed Bilateral Tensor Low-Rank Representation for Clustering
• Authors: Hui Chen, Xinjie Wang, Xianchao Xiu, Wanquan Liu,
• Abstract summary: We propose a novel bilateral low-rank representation model called TBTLRR.<n>It integrates a nuclear norm by arbitrary unitary unitary, allowing for more effective capture of global data.<n>It is able to exploit local correlations between image and latent correlations.
• Score: 17.14664384622625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tensor low-rank representation (TLRR) has demonstrated significant success in image clustering. However, most existing methods rely on fixed transformations and suffer from poor robustness to noise. In this paper, we propose a novel transformed bilateral tensor low-rank representation model called TBTLRR, which introduces a data-adaptive tensor nuclear norm by learning arbitrary unitary transforms, allowing for more effective capture of global correlations. In addition, by leveraging the bilateral structure of latent tensor data, TBTLRR is able to exploit local correlations between image samples and features. Furthermore, TBTLRR integrates the $\ell_{1/2}$-norm and Frobenius norm regularization terms for better dealing with complex noise in real-world scenarios. To solve the proposed nonconvex model, we develop an efficient optimization algorithm inspired by the alternating direction method of multipliers (ADMM) and provide theoretical convergence. Extensive experiments validate its superiority over the state-of-the-art methods in clustering. The code will be available at https://github.com/xianchaoxiu/TBTLRR.

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