# Quantized criterion-based kernel recursive least squares adaptive filtering for time series prediction

arXiv: https://arxiv.org/abs/2307.01442 · HTML: https://mlsanity.com/paper/2307.01442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Generalized Correntropy for Robust Adaptive Filtering](https://mlsanity.com/paper/1504.02931/) (2015)
- [Quantized Minimum Error Entropy Criterion](https://mlsanity.com/paper/1710.04089/) (2017)
- [Minimum Error Entropy Kalman Filter](https://mlsanity.com/paper/1904.06617/) (2019)
- [Minimum Error Entropy Rauch-Tung-Striebel Smoother](https://mlsanity.com/paper/2301.05813/) (2023)

## Abstract (arXiv)

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms.