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Previous matching component analysis (MCA) techniques map two data domains toa common domain for further processing in data fusion and transfer learningcontexts. In this paper, we extend these techniques to the star-graphmultimodal (SGM) case in which one particular data domain is connected to $m$others via an objective function. We provide a particular feasible point forthe resulting trace maximization problem in closed form and algorithms for itscomputation and iterative improvement, leading to our main result, the SGMmaps. We also provide numerical examples demonstrating that SGM is capable ofencoding into its maps more information than MCA when few training points areavailable. In addition, we develop a further generalization of the MCAcovariance constraint, eliminating a previous feasibility condition andallowing larger values of the rank of the prescribed covariance matrix.

Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning