Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

• URL: http://arxiv.org/abs/2208.13669v1
• Date: Mon, 29 Aug 2022 15:14:21 GMT
• Title: Latent Heterogeneous Graph Network for Incomplete Multi-View Learning
• Authors: Pengfei Zhu, Xinjie Yao, Yu Wang, Meng Cao, Binyuan Hui, Shuai Zhao, and Qinghua Hu
• Abstract summary: We propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks.
• Score: 57.49776938934186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To explore the complex relationship between samples and latent representations, a neighborhood constraint and a view-existence constraint are proposed, for the first time, to construct a heterogeneous graph. Finally, to avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model over existing state-of-the-art approaches.

Related papers

• Multi-View Class Incremental Learning [57.14644913531313]
  Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. This paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views.
  arXiv  Detail & Related papers  (2023-06-16T08:13:41Z)
• Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification [78.15629210659516]
  In this paper, we propose an incomplete multi-view partial multi-label classification network named RANK. We break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample. Our model is not only able to handle complete multi-view multi-label datasets, but also works on datasets with missing instances and labels.
  arXiv  Detail & Related papers  (2023-03-30T03:09:25Z)
• Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data [52.491074276133325]
  Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. We propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
  arXiv  Detail & Related papers  (2022-11-08T09:19:32Z)
• A unified framework based on graph consensus term for multi-view learning [5.168659132277719]
  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.
  arXiv  Detail & Related papers  (2021-05-25T09:22:21Z)
• Uncorrelated Semi-paired Subspace Learning [7.20500993803316]
  We propose a generalized uncorrelated multi-view subspace learning framework. To showcase the flexibility of the framework, we instantiate five new semi-paired models for both unsupervised and semi-supervised learning. Our proposed models perform competitively to or better than the baselines.
  arXiv  Detail & Related papers  (2020-11-22T22:14:20Z)
• Deep Partial Multi-View Learning [94.39367390062831]
  We propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets) We fifirst provide a formal defifinition of completeness and versatility for multi-view representation. We then theoretically prove the versatility of the learned latent representations.
  arXiv  Detail & Related papers  (2020-11-12T02:29:29Z)
• Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency [65.76554214664101]
  Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. We propose a new multi-view graph learning framework, which for the first time simultaneously models multi-view consistency and multi-view inconsistency in a unified objective function. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.
  arXiv  Detail & Related papers  (2020-08-24T06:11:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.