K-Means Kernel Classifier

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

8 pages, 2 figures

References in corpus (1)

• Randomized Kernel Methods for Least-Squares Support Vector Machines

Cited by in corpus (1)

• Diffusion Self-Organizing Map on the Hypersphere