Abstract
A new pattern recognition method for classification of multi dimensional samples is proposed. In pattern recognition problems samples (pixels in remote sensing) are described using a number of features (dimensions/bands in remote sensing). While a number of features of the samples are useful for a better description of the image, they pose a threat in terms of unwieldy mass of data. In this paper we propose a method to achieve dimensionality reduction using regression. The method proposed transforms the feature values into representative patterns, termed as symbolic objects, which are obtained through regression lines. The so defined symbolic object accomplishes dimensionality reduction of the data. A new distance measure is devised to measure the distances between the symbolic objects (fitted regression lines) and clustering is preformed. The efficacy of the method is corroborated experimentally.
This research is supported by ISRO project RESPOND 10/4/317, Oct 1999.
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Rangarajan, L., Nagabhushan, P. (2005). Linear Regression for Dimensionality Reduction and Classification of Multi Dimensional Data. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_25
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DOI: https://doi.org/10.1007/11590316_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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