K-Means Kernel Classifier
Machine Learning
2020-12-25 v1 Data Analysis, Statistics and Probability
Abstract
We combine K-means clustering with the least-squares kernel classification method. K-means clustering is used to extract a set of representative vectors for each class. The least-squares kernel method uses these representative vectors as a training set for the classification task. We show that this combination of unsupervised and supervised learning algorithms performs very well, and we illustrate this approach using the MNIST dataset
Keywords
Cite
@article{arxiv.2012.13021,
title = {K-Means Kernel Classifier},
author = {M. Andrecut},
journal= {arXiv preprint arXiv:2012.13021},
year = {2020}
}
Comments
8 pages, 2 figures