Multi-View Learning is a machine learning paradigm that leverages multiple distinct representations (views) of the same data to learn more robust and informative models, exploiting the complementary information and natural redundancy across views to improve prediction accuracy, representation quality, and generalization. Views can arise from different sensors, feature types, modalities, or data transformations that each capture different aspects of the underlying phenomenon.

Why Multi-View Learning Matters in AI/ML: Multi-view learning exploits the complementary and redundant nature of multiple data representations to learn representations that are more robust, complete, and generalizable than any single view, based on the theoretical insight that agreement across views provides a strong learning signal.

• Co-training — The foundational multi-view algorithm: two classifiers are trained on different views, and each classifier's high-confidence predictions on unlabeled data are added as pseudo-labeled training examples for the other; convergence is guaranteed when views are conditionally independent given the label • Multi-kernel learning — Different kernels capture different views of the data; MKL learns an optimal combination of kernels: K = Σ_v α_v K_v, where each kernel K_v represents a view and weights α_v determine view importance; this extends SVMs to multi-view settings • Subspace learning — Methods like Canonical Correlation Analysis (CCA) find shared subspaces where different views are maximally correlated, extracting the common latent structure underlying all views while discarding view-specific noise • View agreement principle — The theoretical foundation: if two views independently predict the same label, that prediction is likely correct; this principle underlies co-training, multi-view consistency regularization, and contrastive multi-view learning • Deep multi-view learning — Neural networks with view-specific encoders and shared fusion layers learn complementary features from each view, with objectives that encourage both view-specific informativeness and cross-view consistency

Multi-view learning provides the theoretical and practical framework for leveraging multiple complementary representations of data, exploiting cross-view agreement and redundancy to learn more robust and generalizable models than single-view approaches, underlying modern techniques from contrastive self-supervised learning to multimodal fusion.