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Dimensionality Reduction Using Factor Analysis

Published in Griffith Research Online (Griffith University, Queensland, Australia) • Jan 1, 2006
NobleIDNI2P72W42R66S07
Authors:
Nitin Khosla

Abstract

In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the origina...

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