Generalized Multi-view Shared Subspace Learning using View Bootstrapping

• URL: http://arxiv.org/abs/2005.06038v1
• Date: Tue, 12 May 2020 20:35:14 GMT
• Title: Generalized Multi-view Shared Subspace Learning using View Bootstrapping
• Authors: Krishna Somandepalli and Shrikanth Narayanan
• Abstract summary: 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. 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. 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.
• Score: 43.027427742165095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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