1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
Jian-wei Liu, Hao-jie Xie, Run-kun Lu +1
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods,…
cs.LG2022★ 1 cited
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
Jian-wei Liu, Xi-hao Ding, Run-kun Lu +1
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 complement…
cs.CV2021
Attentive Multi-View Deep Subspace Clustering Net
Run-kun Lu, Jian-wei Liu, Xin Zuo
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views an…