Kernel logistic regression algorithm for large-scale data classification

Abstract EN

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.