Multi-view clustering (MVC) can exploit the complementary information among multi-view data to achieve the satisfactory performance, thus having extensive potentials for practical applications. Although Nonnegative Matrix Factorization (NMF) has emerged as an effective technique for MVC, the existing NMF-based methods still have two main limitations: 1) They solely focus on the reconstruction of original data, which can be regarded as the decoder of an autoencoder, while neglecting the low-dimensional representation learning. 2) They lack the ability to effectively capture both linear and nonlinear structures of data. To solve these problems, in this paper, we propose a ual uto-weighted multi-view clustering model based on utoencoder-like (DANMF), which enables a comprehensive exploration of both linear and nonlinear structures. Specifically, we establish an autoencoder-like NMF model that learns linear low-dimensional representations by integrating data reconstruction and representation learning within a unified framework. Moreover, the adaptive graph learning is introduced to explore the nonlinear structures in data. We further design a dual auto-weighted strategy to adaptively compute weights for different views and low-dimensional representations, thereby obtaining an enhanced consistent graph. An effective algorithm based on Multiplicative Update Rule (MUR) is developed to solve the DANMF with the theoretical convergence guarantee. Experimental results show that the proposed DANMF can effectively improve the clustering performance compared with the state-of-the-art MVC algorithms.

中文翻译：

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通过类似自动编码器的非负矩阵分解进行双自动加权多视图聚类

多视图聚类（MVC）可以利用多视图数据之间的互补信息来获得令人满意的性能，因此具有广泛的实际应用潜力。尽管非负矩阵分解（NMF）已成为 MVC 的有效技术，但现有的基于 NMF 的方法仍然存在两个主要局限性：1）它们仅关注原始数据的重建，可以将其视为自动编码器的解码器，而忽略了低维表示学习。 2）它们缺乏有效捕获数据的线性和非线性结构的能力。为了解决这些问题，在本文中，我们提出了一种基于类自动编码器（DANMF）的自动加权多视图聚类模型，该模型能够全面探索线性和非线性结构。具体来说，我们建立了一个类似自动编码器的 NMF 模型，通过在统一框架内集成数据重建和表示学习来学习线性低维表示。此外，引入自适应图学习来探索数据中的非线性结构。我们进一步设计了一种双自动加权策略来自适应计算不同视图和低维表示的权重，从而获得增强的一致图。提出了一种基于乘法更新规则（MUR）的有效算法来求解DANMF，并保证理论收敛性。实验结果表明，与最先进的MVC算法相比，所提出的DANMF可以有效提高聚类性能。