LIMITATIONS OF PRINCIPAL COMPONENT ANALYSIS FOR DIMENSIONALITY-REDUCTION FOR CLASSIFICATION OF HYPERPSECTRAL DATA

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" LIMITATIONS OF PRINCIPAL COMPONENT ANALYSIS FOR DIMENSIONALITY-REDUCTION FOR CLASSIFICATION OF HYPERPSECTRAL DATA "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	150625
Doc. No	:	ET22417
Main Entry	:	Anil Cheriyadat
Title Proper	:	LIMITATIONS OF PRINCIPAL COMPONENT ANALYSIS FOR DIMENSIONALITY-REDUCTION FOR CLASSIFICATION OF HYPERPSECTRAL DATA
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	It is a popular practice in the remote-sensing community to apply principalcomponent analysis (PCA) on a higher-dimensional feature space to achievedimensionality-reduction. Several factors that have led to the popularity of PCA includeits simplicity, ease of use, availability as part of popular remote-sensing packages, andoptimal nature in terms of mean square error. These advantages have prompted theremote-sensing research community....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL45633.pdf
Title and statement of responsibility and	:	LIMITATIONS OF PRINCIPAL COMPONENT ANALYSIS FOR DIMENSIONALITY-REDUCTION FOR CLASSIFICATION OF HYPERPSECTRAL DATA [Thesis]