Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization which could explore useful feature representations at lower-dimensional space and therefore alleviate dimension curse. However, there are two major drawbacks in the existing work: i) most matrix factorization methods are limited to shadow depth, which leads to the inability to fully discover the rich hidden information of original data. Few deep matrix factorization methods provide a basis for the selection of the new representation's dimensions of different layers. ii) the majority of current approaches only concentrate on the view-shared information and ignore the specific local features in different views. To tackle the above issues, we propose a novel Multi-View Clustering method with Deep semi-NMF and Global Graph Refinement (MVC-DMF-GGR) in this paper. Firstly, we capture new representation matrices for each view by hierarchical decomposition, then learn a common graph by approximating a combination of graphs which are reconstructed from these new representations to refine the new representations in return. An alternate algorithm with proved convergence is then developed to solve the optimization problem and the results on six multi-view benchmarks demonstrate the effectiveness and superiority of our proposed algorithm.
翻译:在机器学习和数据开采界,多视角分组是一项重要而又具有挑战性的任务。多视角分组的一个流行战略是矩阵集成,它可以探索低维空间的有用特征表现,从而减轻维度诅咒。然而,现有工作中有两个主要缺陷:(1) 多数矩阵集成方法限于影子深度,导致无法充分发现原始数据中隐藏的丰富信息。很少深基集成方法为选择不同层次的新代表层面提供了基础。(2) 目前大多数方法仅集中于共享的视图信息,而忽略不同观点中的具体地方特征。为了解决上述问题,我们提出了与深半NMF和全球图解析(MVC-DMF-GGR)一道的新型多视角组合方法。首先,我们通过等级解析定位为每一种观点收集了新的代表矩阵,然后通过对从这些新表述中重新构建的图表进行匹配,以完善新的表达方式来学习一个共同的图表。随后,我们提出了一种与深半NMMFF和GGRGR(MM-DMF-GGR)相结合的替代算法,以展示了六种优化标准的结果。