Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
This work addresses multi-view learning problems in machine learning, offering a flexible framework that can inspire new models, but it is incremental as it builds upon existing subspace learning techniques.
The authors tackled multi-view subspace learning by proposing a unified framework that learns orthogonal projections for all views, integrating correlations, supervised discriminant capacity, and distance preservation. They demonstrated its versatility with new models for discriminant analysis and multi-label classification, achieving competitive or better results compared to non-orthogonal methods in experiments on real-world datasets.
We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views. The framework integrates the correlations within multiple views, supervised discriminant capacity, and distance preservation in a concise and compact way. It not only includes several existing models as special cases, but also inspires new novel models. To demonstrate its versatility to handle different learning scenarios, we showcase three new multi-view discriminant analysis models and two new multi-view multi-label classification ones under this framework. An efficient numerical method based on successive approximations via eigenvectors is presented to solve the associated optimization problem. The method is built upon an iterative Krylov subspace method which can easily scale up for high-dimensional datasets. Extensive experiments are conducted on various real-world datasets for multi-view discriminant analysis and multi-view multi-label classification. The experimental results demonstrate that the proposed models are consistently competitive to and often better than the compared methods that do not learn orthogonal projections.