# CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines

arXiv: https://arxiv.org/abs/2504.11476 · HTML: https://mlsanity.com/paper/2504.11476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning](https://mlsanity.com/paper/2309.02250/) (2023)
- [Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach](https://mlsanity.com/paper/2404.18101/) (2024)
- [Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.04774/) (2024)
- [Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints](https://mlsanity.com/paper/2011.12659/) (2020)
- [Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning](https://mlsanity.com/paper/1906.08144/) (2019)
- [Flexi-Fuzz least squares SVM for Alzheimer's diagnosis: Tackling noise, outliers, and class imbalance](https://mlsanity.com/paper/2410.14207/) (2024)
- [Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach](https://mlsanity.com/paper/2411.17128/) (2024)

## Abstract (arXiv)

Restricted kernel machines (RKMs) represent a versatile and powerful framework within the kernel machine family, leveraging conjugate feature duality to address a wide range of machine learning tasks, including classification, regression, and feature learning. However, their performance can degrade significantly in the presence of noise and outliers, which compromises robustness and predictive accuracy. In this paper, we propose a novel enhancement to the RKM framework by integrating a class-informed weighted function. This weighting mechanism dynamically adjusts the contribution of individual training points based on their proximity to class centers and class-specific characteristics, thereby mitigating the adverse effects of noisy and outlier data. By incorporating weighted conjugate feature duality and leveraging the Schur complement theorem, we introduce the class-informed restricted kernel machine (CI-RKM), a robust extension of the RKM designed to improve generalization and resilience to data imperfections. Experimental evaluations on benchmark datasets demonstrate that the proposed CI-RKM consistently outperforms existing baselines, achieving superior classification accuracy and enhanced robustness against noise and outliers. Our proposed method establishes a significant advancement in the development of kernel-based learning models, addressing a core challenge in the field.