In this talk, we present a theoretical study on the problem of learning a vector-valued function using generalized TV regularization. We propose a representer theorem that describes the solution set for this problem. Our representer theorem is based on the notion of non-uniform vector-valued L-splines which we introduce and study. At the end, we mention several applications of our representer theorem that can be used.
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