AI RESEARCH
AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
arXiv CS.LG
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ArXi:2603.17610v1 Announce Type: new Multi-view learning primarily aims to fuse multiple features to describe data comprehensively. Most prior studies implicitly assume that different views share similar dimensions. In practice, however, severe dimensional disparities often exist among different views, leading to the unbalanced multi-view learning issue. For example, in emotion recognition tasks, video frames often reach dimensions of $10^6$, while physiological signals comprise only $10^1$ dimensions.