Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization
Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization
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DOI：
10.1109/tnnls.2023.3304626
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发表时间：
2023-09
影响因子：
10.4
通讯作者：
Haonan Huang;Guoxu Zhou;Qianchuan Zhao;Lifang He;Shengli Xie
中科院分区：
文献类型：
--
作者：
Haonan Huang;Guoxu Zhou;Qianchuan Zhao;Lifang He;Shengli Xie
从多视图数据中学习全面的表示在许多实际应用程序中是至关重要的。基于非负矩阵分解(NMF)的多视图表示学习(MRL)通过将高维空间投影到具有很强可解释性的低维空间而被广泛采用。然而，大多数基于NMF的MRL技术都是忽略层次信息的浅模型。尽管最近提出了基于深度矩阵分解(DMF)的方法，但大多数方法只关注多个视图的一致性，且聚类步骤繁琐。为了解决上述问题，本文提出了一种新的模型--深度自动编码型NMF for MRL(DANMF-MRL)，该模型通过深度编码获得表示矩阵，并将其解码成原始数据。这样，通过基于DANMF的框架，我们可以同时考虑多视图的一致性和互补性，允许更全面的表示。在此基础上，我们进一步提出了一种一步法DANMF-MRL，它在统一的框架下学习潜在表示和最终聚类标签矩阵。在该方法中，这两个步骤可以相互协商，充分利用潜在的聚类结构，避免了之前繁琐的聚类步骤，从而获得最优的聚类性能。在此基础上，提出了两种有效的迭代优化算法来求解所提出的模型，并进行了理论上的收敛分析。在五个基准数据集上的大量实验证明了我们的方法相对于其他最先进的MRL方法的优越性。
Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great interpretability. However, most prior NMF-based MRL techniques are shallow models that ignore hierarchical information. Although deep matrix factorization (DMF)-based methods have been proposed recently, most of them only focus on the consistency of multiple views and have cumbersome clustering steps. To address the above issues, in this article, we propose a novel model termed deep autoencoder-like NMF for MRL (DANMF-MRL), which obtains the representation matrix through the deep encoding stage and decodes it back to the original data. In this way, through a DANMF-based framework, we can simultaneously consider the multiview consistency and complementarity, allowing for a more comprehensive representation. We further propose a one-step DANMF-MRL, which learns the latent representation and final clustering labels matrix in a unified framework. In this approach, the two steps can negotiate with each other to fully exploit the latent clustering structure, avoid previous tedious clustering steps, and achieve optimal clustering performance. Furthermore, two efficient iterative optimization algorithms are developed to solve the proposed models both with theoretical convergence analysis. Extensive experiments on five benchmark datasets demonstrate the superiority of our approaches against other state-of-the-art MRL methods.