Two SVDs produce more focal deep learning representations
This work addresses the need for better representation methods in deep learning, though it appears incremental as it builds on existing SVD-based approaches.
The paper tackles the problem of computing efficient and effective deep learning representations by proposing a method using two consecutive SVDs, which results in more focal representations with improved efficiency compared to prior work.
A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities. Much recent progress in the field has focused on efficient and effective methods for computing representations. In this paper, we propose an alternative method that is more efficient than prior work and produces representations that have a property we call focality -- a property we hypothesize to be important for neural network representations. The method consists of a simple application of two consecutive SVDs and is inspired by Anandkumar (2012).