Discriminative metric learning for multi-view graph partitioning. (March 2018)

• Record Type: Journal Article
• Authors: Li, Juan-Hui; Wang, Chang-Dong; Li, Pei-Zhen; Lai, Jian-Huang
• Is Part Of: Pattern recognition. Volume 75(2018:Mar.)
• Page Start: 199
• Page End: 213
• Publication Date: 2018-03
• Subjects: Discriminative metric learning -- Multi-view -- Graph partitioning -- Permanence -- Modularity; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.06.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose discriminative metric learning for multi-view graph partitioning. We envision the multi-view graph as an adaptive dynamic system. The intra-view connections and the inter-view couplings are interplayed. Extensive experiments have been conducted to show the effectiveness. Abstract: In recent years, multi-view graph partitioning has attracted more and more attention, but most efforts have been made to develop graph partitioning approaches directly in the original topological structure. In many real-world applications, graph may contain noisy links and the distance metric may not be so discriminative for revealing cluster structure. This paper addresses the problem of discriminative metric learning for multi-view graph partitioning. In particular, we propose a novel method called Multi-view DML (abbr. of Multi-view Discriminative Metric Learning) to transform the metric space in the original graph into a more discriminative metric space, in which better graph partitioning results will be obtained. We envision the multi-view graph as an adaptive dynamic system, where both the intra-view connections and the inter-view couplings are interplayed to gradually update the relation metric among nodes. On the one hand, the inter-view coupling will be influenced by the intra-view connection between two nodes. On the other hand, the inter-view coupling will also affect the intra-view connection. Such interplay eventually makes the whole graph reach a steady state which has a stronger cluster structure than the original graph. Extensive experiments are conducted on both synthetic and real-world graphs to confirm that the proposed method is able to learn more discriminative metric.