# One Class Restricted Kernel Machines

arXiv: https://arxiv.org/abs/2502.10443 · HTML: https://mlsanity.com/paper/2502.10443/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Disentangled Representation Learning and Generation with Manifold Optimization](https://mlsanity.com/paper/2006.07046/) (2020)
- [Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine](https://mlsanity.com/paper/2102.08443/) (2021)
- [Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern](https://mlsanity.com/paper/2405.07070/) (2024)
- [Robustness analysis of a Maximum Correntropy framework for linear regression](https://mlsanity.com/paper/1703.04829/) (2017)

## Built on by (most cited first, 5 of 5)

- [TRKM: Twin Restricted Kernel Machines for Classification and Regression](https://mlsanity.com/paper/2502.15759/) (2025)
- [Twin Restricted Kernel Machines for Multiview Classification](https://mlsanity.com/paper/2512.15757/) (2025)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network](https://mlsanity.com/paper/2608.16965/) (2026)

## Abstract (arXiv)

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.