Randomized Kernel Methods for Least-Squares Support Vector Machines
This work addresses computational efficiency for kernel methods in classification, but it is incremental as it builds on existing approximation techniques.
The authors tackled the computational scaling of least-squares support vector machines for multi-class classification by proposing randomized block kernel matrix methods, achieving good accuracy and reliable scaling on relatively large datasets.
The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.