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Multi-label classification is particularly challenging in streaming scenarios, where data arrives sequentially. Online kernel methods offer a promising solution. However, prevailing methods rely on a...
arXiv:2607. 27761v1 Announce Type: new Abstract: In recent years, multi-view clustering has attracted widespread research interest.
arXiv:2504. 18455v2 Announce Type: replace-cross Abstract: We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view.
arXiv:2608. 16148v1 Announce Type: new Abstract: Multi-view multi-label feature selection aims to identify a compact and informative feature subset from heterogeneous views while preserving discriminative information for multiple labels.
Unpaired Canonical Correlation Analysis (UCCA) introduces a method for learning linear projections that maximize correlation between views without requiring paired data. By linking the Quadratic Assignment Problem to CCA, the authors derive a practical algorithm that operates solely on unpaired samples. Experiments on real-world multimodal datasets show that UCCA outperforms existing unpaired alignment baselines in recovering true correlations.
The paper introduces Inverted Contrastive Learning for Unsupervised Feature Selection (ICLFS), a method that treats each feature as a sample by inverting the data matrix and applies a contrastive learning framework to learn consistent representations across masked positive views and a shuffled negative view. Feature saliency is derived from the magnitude of projector‑space embeddings, and a Laplacian‑Gated Ranking Correction step refines the ranking by reducing local redundancy. Experiments on 12 benchmark datasets show that ICLFS achieves the best clustering accuracy on 10 datasets compared to both classical and neural baselines, demonstrating the effectiveness of feature‑wise contrastive consistency for unsupervised feature selection.