Clustering unlabeled data with SOMs improves classification of labeled real-world data
Published 25 June 2003
Rozita Dara, Stefan C. Kremer, Deborah Stacey
Citations80
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TL;DR
A self organizing map is used to cluster unlabeled data and to infer possible labelings from the clusters and results are presented for a number of popular real-world benchmark problems from domains other than text.
Abstract
We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network.
Keywords
Computer Science
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