# Sparse Least Squares Low Rank Kernel Machines

## TL;DR

This paper introduces LR-LSSVM, a sparse, low-rank kernel machine framework that enhances computational efficiency and model sparsity, validated through experiments with robust RBF kernels showing competitive or superior performance.

## Contribution

The paper proposes a novel low rank kernel support vector machine framework with a two-step optimization algorithm, improving sparsity and efficiency over existing models.

## Key findings

- Performance is comparable or superior to existing kernel machines.
- The model achieves sparsity and computational efficiency.
- Validated with experiments using robust RBF kernels.

## Abstract

A general framework of least squares support vector machine with low rank kernels, referred to as LR-LSSVM, is introduced in this paper. The special structure of low rank kernels with a controlled model size brings sparsity as well as computational efficiency to the proposed model. Meanwhile, a two-step optimization algorithm with three different criteria is proposed and various experiments are carried out using the example of the so-call robust RBF kernel to validate the model. The experiment results show that the performance of the proposed algorithm is comparable or superior to several existing kernel machines.