Probabilistic Semi-Canonical Correlation Analysis
Chie Kamada, Asako Kanezaki, Tatsuya Harada · 2015
anonical Correlation Analysis (CCA) requires paired multimodal data to ascertain the relation between two variables. However, it is generally difficult to collect a sufficient amount of paired data of two variables as training samples. This fact leads individual samples of unpaired variables to be additional resources for learning CCA, which are not only able to increase the number of training samples; they are also effective to remove the learning bias caused by the variables' missing patterns. As described in this paper, we propose a novel model of probabilistic CCA by considering the mechanism of data missing. Our method enables widespread applications such as semi-supervised learning via partially labeled training samples and analysis of sensory data which are lacking under certain circumstances. We demonstrate the superior performance of parameter estimation as well as an application of image annotation, compared with existing methods.