Canonical Divergence Analysis
This addresses a practical problem in domains like biology and architecture where data vectors often lack joint distributions or have mismatched attributes.
The paper tackles the problem of analyzing relationships between two random vectors with different attributes, realizations, and potentially no joint distribution, which existing methods cannot handle. It proposes Canonical Divergence Analysis (CDA) with three practical instantiations, showing its potential through extensive empirical evaluation.
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.