GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
Guest Editor's Introduction: Visual Data Mining
IEEE Computer Graphics and Applications
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
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Feature selection (FS) has long been studied in classification and regression problems. In comparison, FS for unsupervised learning has received far less attention. For many real problems concerning unsupervised data clustering, FS becomes an issue of paramount importance. An unsupervised FS method for Gaussian Mixture Models, based on Feature Relevance Determination (FRD), was recently defined. Unfortunately, the data visualization capabilities of general mixture models are limited. Generative Topographic Mapping (GTM), a constrained mixture model, was originally defined to overcome such limitation. In this brief study, we test in some detail the capabilities of a recently described FRD method for GTM that allows the clustering results to be intuitively visualized and interpreted in terms of a reduced subset of selected relevant features.