# Kernel Random Projection Depth for Outlier Detection

arXiv: https://arxiv.org/abs/2306.07056 · HTML: https://mlsanity.com/paper/2306.07056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep Random Projection Outlyingness for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2106.15307/) (2021)

## Built on by (most cited first, 1 of 1)

- [Kernel Outlier Detection](https://mlsanity.com/paper/2506.22994/) (2025)

## Abstract (arXiv)

This paper proposes an extension of Random Projection Depth (RPD) to cope with multiple modalities and non-convexity on data clouds. In the framework of the proposed method, the RPD is computed in a reproducing kernel Hilbert space. With the help of kernel principal component analysis, we expect that the proposed method can cope with the above multiple modalities and non-convexity. The experimental results demonstrate that the proposed method outperforms RPD and is comparable to other existing detection models on benchmark datasets regarding Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).