Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some methods address it by introducing bipartite graphs, they may result in sub-optimal solutions caused by an unstable anchor selection process. (3) They generally ignore the negative impact of latent semantic-unrelated information in each view. To tackle these issues, we propose a new approach termed fast Disentangled Slim Tensor Learning (DSTL) for multi-view clustering . Instead of focusing on the multi-view graph structures, DSTL directly explores the high-order correlations among multi-view latent semantic representations based on matrix factorization. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Subsequently, two slim tensors are constructed with tensor-based regularization. To further enhance the quality of feature disentanglement, the semantic-related representations are aligned across views through a consensus alignment indicator. Our proposed model is computationally efficient and can be solved effectively. Extensive experiments demonstrate the superiority and efficiency of DSTL over state-of-the-art approaches. The code of DSTL is available at https://github.com/dengxu-nju/DSTL.

翻译：基于张量的多视图聚类方法因其在探索跨视图高阶相关性方面的卓越能力而受到广泛关注。然而，现有方法仍存在一些局限性：(1) 多数方法聚焦于不同亲和矩阵间的相关性分析，导致其难以扩展至大规模数据；(2) 尽管部分方法通过引入二分图解决可扩展性问题，但不稳定的锚点选择过程可能导致次优解；(3) 现有方法普遍忽略了各视图中潜在语义无关信息的负面影响。为解决上述问题，本文提出一种称为快速解耦精简张量学习的新方法用于多视图聚类。与关注多视图图结构的方法不同，DSTL 基于矩阵分解直接探索多视图潜在语义表示间的高阶相关性。为缓解特征冗余的负面影响，受鲁棒主成分分析启发，DSTL 将每个视图的潜在低维表示解耦为语义无关部分和语义相关部分。随后，通过基于张量的正则化构建两个精简张量。为进一步提升特征解耦质量，语义相关表示通过共识对齐指示器进行跨视图对齐。所提模型计算高效且可有效求解。大量实验证明 DSTL 在性能和效率上均优于现有先进方法。DSTL 代码已公开于 https://github.com/dengxu-nju/DSTL。