Canonical Divergence Analysis
Machine Learning
2015-11-12 v1 Machine Learning
Abstract
We aim to analyze the relation between two random vectors that may potentially have both different number of attributes as well as realizations, and which may even not have a joint distribution. This problem arises in many practical domains, including biology and architecture. Existing techniques assume the vectors to have the same domain or to be jointly distributed, and hence are not applicable. To address this, we propose Canonical Divergence Analysis (CDA). We introduce three instantiations, each of which permits practical implementation. Extensive empirical evaluation shows the potential of our method.
Keywords
Cite
@article{arxiv.1510.08370,
title = {Canonical Divergence Analysis},
author = {Hoang-Vu Nguyen and Jilles Vreeken},
journal= {arXiv preprint arXiv:1510.08370},
year = {2015}
}
Comments
Submission to AISTATS 2016