# A review of learning vector quantization classifiers

arXiv: https://arxiv.org/abs/1509.07093 · HTML: https://mlsanity.com/paper/1509.07093/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2015.

## Built on by (most cited first, 11 of 11)

- [Prototype Guided Federated Learning of Visual Feature Representations](https://mlsanity.com/paper/2105.08982/) (2021)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing](https://mlsanity.com/paper/2103.05267/) (2021)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)
- [Explaining Reject Options of Learning Vector Quantization Classifiers](https://mlsanity.com/paper/2202.07244/) (2022)
- [Harmonic Loss Trains Interpretable AI Models](https://mlsanity.com/paper/2502.01628/) (2025)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices](https://mlsanity.com/paper/2102.00667/) (2021)
- [Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data](https://mlsanity.com/paper/2109.13610/) (2021)
- [Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces](https://mlsanity.com/paper/2401.12842/) (2024)
- [FastODT: A tree-based framework for efficient continual learning](https://mlsanity.com/paper/2603.13276/) (2026)

## Abstract (arXiv)

In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.