MICA: multimodal independent component analysis
Shotaro Akaho, Yuuto KIUCHI, Shinji Umeyama · 2003
We propose MICA (multimodal independent component analysis) that extends ICA (independent component analysis) to the case that there is a pair of information sources. MICA extracts statistically dependent pairs of features from the sources, where the components of feature vector extracted from each source are independent. Therefore, the cost function is constructed to optimize this degree of pairwise dependence as well as optimizing the cost function of ICA. We approximate the cost function by two dimensional Gram-Charlier expansion and propose a gradient descent algorithm derived by Amari's natural gradient The relation between MICA and traditional CCA (canonical correlation analysis) is similar to the relation between ICA and PCA (principal component analysis).