# Generalized Reference Kernel for One-class Classification

arXiv: https://arxiv.org/abs/2205.00534 · HTML: https://mlsanity.com/paper/2205.00534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 14 of 14)

- [Learning and Evaluating Representations for Deep One-class Classification](https://mlsanity.com/paper/2011.02578/) (2020)
- [Revisiting the Nystrom Method for Improved Large-Scale Machine Learning](https://mlsanity.com/paper/1303.1849/) (2013)
- [Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees](https://mlsanity.com/paper/1804.09893/) (2018)
- [A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent](https://mlsanity.com/paper/2006.05013/) (2020)
- [Faster Kernel Ridge Regression Using Sketching and Preconditioning](https://mlsanity.com/paper/1611.03220/) (2016)
- [Convolutional Kernel Networks for Graph-Structured Data](https://mlsanity.com/paper/2003.05189/) (2020)
- [End-to-End Kernel Learning with Supervised Convolutional Kernel Networks](https://mlsanity.com/paper/1605.06265/) (2016)
- [Hybrid Random Features](https://mlsanity.com/paper/2110.04367/) (2021)
- [Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification](https://mlsanity.com/paper/2001.08873/) (2020)
- [Multimodal Subspace Support Vector Data Description](https://mlsanity.com/paper/1904.07698/) (2019)
- [Boosting rare benthic macroinvertebrates taxa identification with one-class classification](https://mlsanity.com/paper/2002.10420/) (2020)
- [Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification](https://mlsanity.com/paper/1805.07892/) (2018)
- [Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)](https://mlsanity.com/paper/1904.06491/) (2019)
- [Null Space Analysis for Class-Specific Discriminant Learning](https://mlsanity.com/paper/1908.04562/) (2019)

## Built on by (most cited first, 2 of 2)

- [Linear-time One-Class Classification with Repeated Element-wise Folding](https://mlsanity.com/paper/2408.11412/) (2024)
- [Generalized Reference Kernel With Negative Samples For Support Vector One-class Classification](https://mlsanity.com/paper/2506.14895/) (2025)

## Abstract (arXiv)

In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities to approximate kernels, random mappings, and Non-linear Projection Trick. Focusing on small-scale one-class classification, our analysis and experimental results show that the new formulation provides approaches to regularize, adjust the rank, and incorporate additional information into the kernel itself, leading to improved one-class classification accuracy.