On Measure Transformed Canonical Correlation Analysis

In this paper linear canonical correlation analysis (LCCA) is generalized by applying a structured transform to the joint probability distribution of the considered pair of random vectors, i.e., a transformation of the joint probability measure defined on their joint observation space. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the data after transformation of the joint probability measure. We show that judicious choice of the transform leads to a modified canonical correlation analysis, which, in contrast to LCCA, is capable of detecting non-linear relationships between the considered pair of random vectors. Unlike kernel canonical correlation analysis, where the transformation is applied to the random vectors, in MTCCA the transformation is applied to their joint probability distribution. This results in performance advantages and reduced implementation complexity. The proposed approach is illustrated for graphical model selection in simulated data having non-linear dependencies, and for measuring long-term associations between companies traded in the NASDAQ and NYSE stock markets

Audio-visual synchronization recovery in multimedia content

This paper proposes a method recovering audio-visual synchronization of multimedia content. It exploits the correlation between the acoustic and the visual signals in order to estimate the audio-visual drift existing in the content. By shifting the audio signal relative to the visual signal, the estimation of the drift is obtained by searching for the shift producing the maximal audio-visual correlation. We consider two correlation measures, namely, mutual information and canonical correlation, and compare their performance. Experimental results demonstrate that the method using the canonical correlation is effective in recovering the audio-visual synchronization for both speech and non-speech sequences. Index Terms — Audio-visual synchronization, mutual information, canonical correlation, multimedia 1