Dictionary learning based nonlinear classifier training from distributed data

This paper addresses the problem of collaborative training of nonlinear classifiers using big, distributed training data. The supervised learning strategy considered in this paper corresponds to data-driven joint learning of a nonlinear transformation that maps the (training) data to a higher-dimensional feature space and a ridge regression based linear classifier in the feature space. The key aspect of this paper, which distinguishes it from related prior work, is that it assumes: (i) the training data are distributed across a number of interconnected sites, and (ii) sizes of the local training data as well as privacy concerns prohibit exchange of individual training samples between sites. The main contribution of this paper is formulation of an algorithm, termed cloud D-KSVD, that reliably, efficiently and collaboratively learns both the nonlinear map and the linear classifier under these constraints. In order to demonstrate the effectiveness of cloud D-KSVD, a number of numerical experiments on the MNIST dataset are also reported in the paper.