One Class Restricted Kernel Machines

TL;DR

This paper introduces the one-class restricted kernel machine (OCRKM), a novel robust model that combines kernel methods with one-class classification to improve outlier detection and generalization in machine learning tasks.

Contribution

The paper proposes OCRKM, integrating one-class classification with RKM using an energy function inspired by RBM, enhancing robustness against outliers.

Findings

OCRKM outperforms baseline models on UCI datasets.

Experimental results show improved outlier detection.

Statistical analysis confirms superior generalization.

Abstract

Restricted kernel machines (RKMs) have demonstrated a significant impact in enhancing generalization ability in the field of machine learning. Recent studies have introduced various methods within the RKM framework, combining kernel functions with the least squares support vector machine (LSSVM) in a manner similar to the energy function of restricted boltzmann machines (RBM), such that a better performance can be achieved. However, RKM's efficacy can be compromised by the presence of outliers and other forms of contamination within the dataset. These anomalies can skew the learning process, leading to less accurate and reliable outcomes. To address this critical issue and to ensure the robustness of the model, we propose the novel one-class RKM (OCRKM). In the framework of OCRKM, we employ an energy function akin to that of the RBM, which integrates both visible and hidden variables in a nonprobabilistic setting. The formulation of the proposed OCRKM facilitates the seamless integration of one-class classification method with the RKM, enhancing its capability to detect outliers and anomalies effectively. The proposed OCRKM model is evaluated over UCI benchmark datasets. Experimental findings and statistical analyses consistently emphasize the superior generalization capabilities of the proposed OCRKM model over baseline models across all scenarios.