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Linear Regression for Dimensionality Reduction and Classification of Multi Dimensional Data

  • Lalitha Rangarajan
  • P. Nagabhushan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

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.

Keywords

Pattern Classification Dimensionality Reduction Feature Sequence Regression Clustering Data Assimilation Multi Dimensional Data Symbolic Data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lalitha Rangarajan
    • 1
  • P. Nagabhushan
    • 1
  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia

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