Constructive Analysis for Least Squares Regression with Generalized K-Norm Regularization

Abstract

We introduce a constructive approach for the least squares algorithms with generalized K-norm regularization. Different from the previous studies, a stepping-stone function is constructed with some adjustable parameters in error decomposition. It makes the analysis flexible and may be extended to other algorithms. Based on projection technique for sample error and spectral theorem for integral operator in regularization error, we finally derive a learning rate.