Simple and robust methods for support vector expansions

This work proposes an iterative algorithm that can be simply implemented for finding a sparse solution of a linear system and compares the classical SV approach with other, recently proposed, cross-correlation based, alternative methods.

Abstract

Most support vector (SV) methods proposed in the recent literature can be viewed in a unified framework with great flexibility in terms of the choice of the kernel functions and their constraints. We show that all these problems can be solved within a unique approach if we are equipped with a robust method for finding a sparse solution of a linear system. Moreover, for such a purpose, we propose an iterative algorithm that can be simply implemented. Finally, we compare the classical SV approach with other, recently proposed, cross-correlation based, alternative methods. The simplicity of their implementation and the possibility of exactly calculating their computational complexity constitute important advantages in a real-time signal processing scenario.