2022/01/01 by Jian-wei Liu, Jian–wei Liu, Xi-hao Ding +7 · 1 citation
Computer Science · #Artificial intelligence #Complementarity (molecular biology) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer security #Consistency (knowledge bases) #Diversity (politics) #Domain Adaptation and Few-Shot Learning #Exploit #FOS: Computer and information sciences #Feature learning #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine learning #Multimodal Machine Learning Applications #Political science #Representation (politics) #Sociology #Theoretical computer science #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2201.00168
published in arXiv (Cornell University) (Cornell University)
arxiv created 2022/01/01 · openalex publication_date 2022/01/01 · arxiv updated 2022/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with single complementarity rather than complementary information with diversity. In this paper, to utilize both complementarity and consistency simultaneously, give free rein to the potential of deep learning in grasping diversity-promoting complementarity for multi-view representation learning, we propose a novel supervised multi-view representation learning algorithm, called Self-Attention Multi-View network with Diversity-Promoting Complementarity (SAMVDPC), which exploits the consistency by a group of encoders, uses self-attention to find complementary information entailing diversity. Extensive experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method, and show its superiority over several baseline methods, which only consider single complementary information.