Kernel Logistic Regression Algorithm for Large-Scale Data Classification

Kernel Logistic Regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in large-scale data classification problems and this is mainly because it is computationally expensive. In this paper, we present a new KLR algorithm based on Truncated Regularized Iteratively Re-weighted Least Squares(TR-IRLS) algorithm to obtain sparse large-scale data classification in short evolution time. This new algorithm is called Nystrom Truncated Kernel Logistic Regression (NTR-KLR). The performance achieved using NTR-KLR algorithm is comparable to that of Support Vector Machines (SVMs) methods. The advantage is NTR-KLR can yield probabilistic outputs and its extension to the multi class case is well defined. In addition, its computational complexity is lower than that of SVMs methods and it is easy to implement.