Theory-Driven Label-Specific Representation for Incomplete Multi-View Multi-Label Learning

Quanjiang Li, Tianxiang Xu, Tingjin Luo, Yan Zhong, Yang Li, Yiyun Zhou, Chenping Hou

Keywords: Multi-View Multi-Label Learning

TL;DR: To tackle the complex yet highly practical challenges, we propose a Theory-Driven Label-Specific Representation (TDLSR) framework.

Abstract:

Multi-view multi-label learning typically suffers from dual data incompleteness
due to limitations in feature storage and annotation costs. The interplay of hetero
geneous features, numerous labels, and missing information significantly degrades
model performance. To tackle the complex yet highly practical challenges, we
propose a Theory-Driven Label-Specific Representation (TDLSR) framework.
Through constructing the view-specific sample topology and prototype association
graph, we develop the proximity-aware imputation mechanism, while deriving class
representatives that capture the label correlation semantics. To obtain semantically
distinct view representations, we introduce principles of information shift, inter
action and orthogonality, which promotes the disentanglement of representation
information, and mitigates message distortion and redundancy. Besides, label
semantic-guided feature learning is employed to identify the discriminative shared
and specific representations and refine the label preference across views. Moreover,
we theoretically investigate the characteristics of representation learning and the
generalization performance. Finally, extensive experiments on public datasets and
real-world applications validate the effectiveness of TDLSR.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 6044