Clustering of data and nearest neighbors search for pattern recognition with dimensionality reduction using random projections

Authors:
Ewa Skubalska-Rafajłowicz
Affiliations:
Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wrocław, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

The dimensionality and the amount of data that need to be processed when intensive data streams are classified may occur prohibitively large. The aim of this paper is to analyze Johnson-Linden-strauss type random projections as an approach to dimensionality reduction in pattern classification based on K-nearest neighbors search. We show that in-class data clustering allows us to retain accuracy recognition rates obtained in the original high-dimensional space also after transformation to a lower dimension.