# Robust Learning with Kernel Mean p-Power Error Loss

arXiv: https://arxiv.org/abs/1612.07019 · HTML: https://mlsanity.com/paper/1612.07019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation](https://mlsanity.com/paper/2206.10075/) (2022)
- [IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.15193/) (2023)
- [Error Loss Networks](https://mlsanity.com/paper/2106.03722/) (2021)
- [Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning](https://mlsanity.com/paper/2208.03663/) (2022)

## Abstract (arXiv)

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (CLoss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve consistently better performance when compared with some existing methods.