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Document Type : Latin Dissertation
Language of Document : English
Record Number : 148545
Doc. No : ET20337
Main Entry : HUIWEN ZENG
Title Proper : DIMENSIONALITY REDUCTION AND FEATURE SELECTION USING A MIXED-NORM PENALTY FUNCTION
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : ZENG, HUIWEN. DIMENSIONALITY REDUCTION AND FEATURE SELECTION US-ING A MIXED-NORM PENALTY FUNCTION. (Under the direction of Professor H. JoelTrussell).Dimensionality reduction, which is the process of mapping high-dimension patterns tolower dimension subspaces, is a key issues in enhancing the processing eciency of highdimensional data such as hyperspectral images. Dimensionality reduction has been widelydiscussed in the areas of data mining, image processing, pattern recognition, etc. Becausein most situations, many of the dimensions are redundant or unnecessary for the tasks ofinterest, removing those dimensionality will produce more ecient computation while main-taining the original performance. Dimensionality reduction also reduces the measurementand storage requirements, reduces traini.
Subject : Electericl tess
: برق
electronic file name : TL43464.pdf
Title and statement of responsibility and : DIMENSIONALITY REDUCTION AND FEATURE SELECTION USING A MIXED-NORM PENALTY FUNCTION [Thesis]
 
 
 
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