K-Means Kernel Classifier
This paper presents an incremental method for image classification, potentially benefiting researchers working with hybrid unsupervised-supervised learning approaches.
This paper combines K-means clustering with a least-squares kernel classifier. K-means is used to select representative vectors for each class, which then serve as the training set for the kernel classifier. The approach is demonstrated on the MNIST dataset.
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