Multiple Kernel Regression with Sparsity Constraints
Shayan Aziznejad

Meeting • 18 June 2019

Abstract
We consider the problem of learning a function from a sequence of its noisy samples in a continuous-domain hybrid search space. We adopt the generalized total-variation norm as a sparsity-promoting regularization term to make the problem well-posed. We prove that the solution of this problem admits a sparse kernel expansion with adaptive positions. We also show that the sparsity of the solution is upper-bounded by the number of data points. This allows for an enlargement of the search space and ensures the well-posedness of the problem.