Fast projection pursuit based on quality of projected clusters

Authors:
Marek Grochowski;Włodzisław Duch
Affiliations:
Department of Informatics, Nicolaus Copernicus University, Toruń, Poland;Department of Informatics, Nicolaus Copernicus University, Toruń, Poland and School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Year:
2011

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Abstract

Projection pursuit index measuring quality of projected clusters (QPC) introduced recently optimizes projection directions by minimizing leave-one-out error searching for pure localized clusters. QPC index has been used in constructive neural networks to discover non-local clusters in high-dimensional multiclass data, reduce dimensionality, aggregate features, visualize and classify data. However, for n training instances such optimization requires O(n2) calculations. Fast approximate version of QPC introduced here obtains results of similar quality with O(n) effort, as illustrated in a number of classification and data visualization problems.