Contrastive Multiview Low-Rank Latent Subspace Self-Representation and Classification Network
提出一种端到端的多视图判别学习框架，通过对比学习增强类内一致性、区分类间差异，并利用低秩潜在自表示结构捕捉数据内在关系，在多种分类任务中表现优异，尤其在小样本下保持鲁棒性。
Multiview data classification remains a challenging problem in machine learning, particularly in effectively integrating and representing data from different views. This article introduces contrastive multiview low-rank latent subspace self-representation and classification network (CMvLSCN), a novel end-to-end multiview discriminant learning framework that addresses classification from view, sample, and subspace levels. CMvLSCN employs contrastive learning to enhance interview consistency within categories while differentiating between categories. It imposes a low-rank latent self-representation structure on the unified subspace, capturing intrinsic data relationships. Additionally, sample-level contrastive constraints in the latent space further boost the representation’s discriminative power. Extensive experiments demonstrate CMvLSCN’s superior performance across various multiview classification tasks, notably maintaining robustness even with limited training data. Our code and datasets are publicly available on https://github.com/DeyuTsang/CMvLSCN