隐子空间引导的不完整多视图图补全与聚类

Latent Subspace-Guided Incomplete Multi-view Graph Completion and Clustering

摘要

针对不完整多视图聚类未缺失数据对象间显式关系不可靠,存在一致性偏差的问题,提出一种共享隐含子空间引导的不完整多视图图补全及聚类算法.首先,将补全矩阵和不完 整多视图数据联合映射到共享隐含子空间;其次,借助隐子空间自表示矩阵引导各视图邻接 图学习,使其保持潜在一致性;再次,采用图Laplace正则,使补全矩阵遵循视图流形保留多 视图互补性;最后,将一致性表示和缺失补全集成到一个框架,使不完整多视图数据的补全 和聚类相互促进.在5个常用公开数据集上的实验结果验证了该算法在不完全多视图聚类中 的有效性.

Abstract

To address the unreliable explicit relationships and consistency deviations among non-missing data objects in incomplete multi-view clustering, we propose a shared latent subspace-guided graph completion and clustering algorithm. Secondly, the learning of each view’s adjacency graph is guided by the subspace self-representation matrix to preserve latent consistency. Then, graph Laplacian regularization is applied to ensure that the view manifold is followed by the completed graph, by which multi-view data complementarity is maintained. Finally, consistency representation and missing data completion are integrated into a unified framework, enabling mutual enhancement between completion and clustering. Experimental results on five commonly used public datasets demonstrate the effectiveness of the proposed algorithm for incomplete multi-view clustering.

关键词

多视图聚类 / 不完整数据 / 图补全 / 隐子空间 / 互补性

Key words

multi-view clustering / incomplete data / graph completion / latent subspace / complementarity

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基金资助

国家自然科学基金(62172293)