Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to effectively perform dimension reduction and clustering. However, the existing approaches can be further improved with following considerations: i) The current one-layer matrix factorization framework cannot fully exploit the useful data representations. ii) Most algorithms only focus on the shared information while ignore the view-specific structure leading to suboptimal solutions. iii) The partition level information has not been utilized in existing work. To solve the above issues, we propose a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment. To be specific, the partition representations of each view are obtained through deep matrix decomposition, and then are jointly utilized with the optimal partition representation for fusing multi-view information. Finally, an alternating optimization algorithm is developed to solve the optimization problem with proven convergence. The comprehensive experimental results conducted on six benchmark multi-view datasets clearly demonstrates the effectiveness of the proposed algorithm against the SOTA methods.
翻译:近些年来,为了收集多种来源信息,对多层集成进行了广泛的研究; 一种典型的多层集成方法是以矩阵集成为基础的,以便有效地进行尺寸缩减和分组; 但是,现有的方法可以进一步改进,其考虑因素如下:(一) 目前的单层矩阵集成框架不能充分利用有用的数据表述方式。 (二) 大多数算法仅侧重于共享信息,而忽视导致次优化解决方案的视特定结构。 (三) 现有工作没有利用分区水平信息。为了解决上述问题,我们建议通过深层矩阵分解和分区对齐,采用新的多层集成算法。具体地说,每种观点的分区表示方式都是通过深层矩阵分解式获得的,然后与利用多层信息的最佳分区表示方式共同使用。最后,开发了一种交替优化算法,以解决最优化问题,并证明是趋同的。在六个基准多视图数据集上进行的全面试验的结果清楚地表明了拟议的算法相对于SOTA方法的有效性。