Adaptive Instance-wise Multi-view Clustering

Shudong Huang, Hecheng Cai, Hao Dai, Wentao Feng, Jiancheng Lv

Abstract:

Multi-view clustering has garnered attention for its effectiveness in addressing heterogeneous data by unsupervisedly revealing underlying correlations between different views.
As a mainstream method, multi-view graph clustering has attracted increasing attention in recent years. Despite its success, it still has some limitations. Notably, many methods construct the similarity graph without considering the local geometric structure and exploit
coarse-grained complementary and consensus information from different views at the view level.
To solve the shortcomings, we focus on local structure consistency and fine-grained representations across multiple views. Specifically, each view's local consistency similarity graph is obtained through the adaptive neighbor. Subsequently, the multi-view similarity tensor is rotated and sliced into fine-grained instance-wise slices. Finally, these slices are fused into the final similarity matrix. Consequently, cross-view consistency can be captured by exploring the intersections of multiple views in an instance-wise manner. We design a collaborative framework with the augmented Lagrangian method to refine all subtasks towards optimal solutions iteratively. Extensive experiments on several multi-view datasets confirm the significant enhancement in clustering accuracy achieved by our method.

Primary Subject Area: [Content] Multimodal Fusion

Relevance To Conference: We propose a new multi-view clustering algorithm, which has wide applications in many fields. In image processing, multi-view clustering can be used for image segmentation, object detection, and recognition tasks. In speech recognition, multi-view clustering can be used for speech signal classification and recognition. Multi-view news data clustering analysis can quickly obtain valuable information from massive news, and can achieve good application results in the fields of public opinion analysis, personalized news recommendation, sentiment analysis, early warning, and so on.

Submission Number: 3469