Generalized Multi-view Shared Subspace Learning using View Bootstrapping
This addresses multi-view learning challenges for applications like recognition tasks, but appears incremental as it builds on existing correlation-based methods with a bootstrapping approach.
The paper tackles the problem of modeling hundreds of views per event and learning robust multi-view embeddings without knowledge of view acquisition, using a neural method based on multi-view correlation with view bootstrapping, achieving robustness in tasks like spoken word recognition, 3D object classification, and pose-invariant face recognition.
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks. In this context, two open research questions remain: How can we model hundreds of views per event? Can we learn robust multi-view embeddings without any knowledge of how these views are acquired? We present a neural method based on multi-view correlation to capture the information shared across a large number of views by subsampling them in a view-agnostic manner during training. To provide an upper bound on the number of views to subsample for a given embedding dimension, we analyze the error of the bootstrapped multi-view correlation objective using matrix concentration theory. Our experiments on spoken word recognition, 3D object classification and pose-invariant face recognition demonstrate the robustness of view bootstrapping to model a large number of views. Results underscore the applicability of our method for a view-agnostic learning setting.