Least-squares regularized regression with dependent samples and q-penalty

Abstract

Least-squares regularized learning algorithms for regression were well-studied in the literature when the sampling process is independent and the regularization term is the square of the norm in a reproducing kernel Hilbert space (RKHS). Some analysis has also been done for dependent sampling processes or regularizers being the qth power of the function norm (q-penalty) with 0 < q ≤ 2. The purpose of this article is to conduct error analysis of the least-squares regularized regression algorithm when the sampling sequence is weakly dependent satisfying an exponentially decaying α-mixing condition and when the regularizer takes the q-penalty with 0 < q ≤ 2. We use a covering number argument and derive learning rates in terms of the α-mixing decay, an approximation condition and the capacity of balls of the RKHS.

Keywords:

• learning theory
• regularization scheme in reproducing kernel Hilbert spaces
• q-penalty
• approximation error condition
• covering number
• α-mixing weakly dependent sampling sequence

AMS Subject Classification::

• 68T05
• 62J02