A unified framework based on graph consensus term for multi-view learning

• URL: http://arxiv.org/abs/2105.11781v1
• Date: Tue, 25 May 2021 09:22:21 GMT
• Title: A unified framework based on graph consensus term for multi-view learning
• Authors: Xiangzhu Meng, Lin Feng, Chonghui Guo
• Abstract summary: We propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula. Our method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods. To this end, the diversity and complementary information among different views could be simultaneously considered.
• Score: 5.168659132277719
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In recent years, multi-view learning technologies for various applications have attracted a surge of interest. Due to more compatible and complementary information from multiple views, existing multi-view methods could achieve more promising performance than conventional single-view methods in most situations. However, there are still no sufficient researches on the unified framework in existing multi-view works. Meanwhile, how to efficiently integrate multi-view information is still full of challenges. In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term. In particular, our method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods. Meanwhile, we choose heterogeneous graphs to construct the graph consensus term to explore the correlations among multiple views jointly. To this end, the diversity and complementary information among different views could be simultaneously considered. Furthermore, the proposed framework is utilized to implement the multi-view extension of Locality Linear Embedding, named Multi-view Locality Linear Embedding (MvLLE), which could be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.

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