Kernel method for clustering based on optimal target vector
Disordered Systems and Neural Networks
2009-11-11 v1 Data Analysis, Statistics and Probability
Quantitative Methods
Abstract
We introduce the notion of optimal target vector, and describe how it creates a link between supervised and unsupervised learning. We exploit this notion to construct Ising models, for dichotomic clustering, whose couplings are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. The effectiveness of the method is shown in the case of the well known iris data-set and in benchmarks of gene expression levels.
Cite
@article{arxiv.cond-mat/0511630,
title = {Kernel method for clustering based on optimal target vector},
author = {Leonardo Angelini and Daniele Marinazzo and Mario Pellicoro and Sebastiano Stramaglia},
journal= {arXiv preprint arXiv:cond-mat/0511630},
year = {2009}
}
Comments
4 pages, 4 figures