Dimensionality reduction by minimizing nearest-neighbor classification error

Authors:
Mauricio Villegas;Roberto Paredes
Affiliations:
Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Camino de Vera s/n, Edif. 8G Acc. B, 46022 Valencia, Spain;Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Camino de Vera s/n, Edif. 8G Acc. B, 46022 Valencia, Spain
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques.