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Aug 28, 2024 � A consistent and specific learning framework is proposed to handle MVML data. We exploit the multi-view consistency by leveraging low-rank constraint.
Aug 28, 2024 � A consistent and specific learning framework is proposed to handle MVML data. We exploit the multi-view consistency by leveraging low-rank constraint.
May 22, 2024 � This paper proposes an MVML learning method based on the inconsistent shared features extracted by the graph attention model.
In this paper, a novel multi-view multi-label learning approach named SIMM is pro- posed which leverages shared subspace exploita- tion and view-specific�...
Missing: Consistent correlation
Nov 4, 2022 � This paper proposes a non-aligned multi-view multi-label classification method that learns view-specific labels (LVSL), aiming to explicitly mine the�...
The joint low-rank constraint enables the view-specific learner to exploit other views to help improve the performance, without accessing the features of other�...
Self-representation means that each data sample is expressed by a linear combination of other samples in the same subspace. 3.1 AAAI18 Consistent and Specific�...
Mar 13, 2023 � We propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper.
The consistency part is effectively acquired through dimension reduction to the label matrix (Zhang et al. 2020b), or considers the relation between labels to�...
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer su- pervision information than single-label,�...