In many applications, such as classification of images or videos, it is interest to develop a framework for tensor data instead an ad-hoc way transforming vectors due the comput...

Classical canonical correlation analysis (CCA) requires matrices to be low dimensional, i.e. the number of features cannot exceed sample size. Recent developments in CCA have mainly focused on high-dimensional setting, where both under greatly exceeds These approaches impose penalties optimization problems that are needed solve iteratively, and estimate multiple vectors sequentially. In this wo...