Principal Component Analysis for Feature Reduction
What is Principal Component Analysis? Principal component analysis (PCA) Reduce the dimensionality of a data set by finding a new set of variables, smaller

http://www.public.asu.edu/~jye02/CLASSES/Spring-2007/NOTES/Lec15-PCA.ppt

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Principal Component Analysis
Feature reduction refers to the mapping of the original high-dimensional data onto a lower Principal Component Analysis for clustering gene expression

http://www.public.asu.edu/~jye02/CLASSES/Fall-2007/NOTES/PCA.ppt

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Principal Component Analysis & Multidimensional scaling
Dimensional Reduction and feature selection . Microarray and Proteomics data analysis, Verona 2004, Carsten Friis . Principal Component Analysis (PCA)

http://www.biostat.ucla.edu/course/m278/shpca.ppt

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2806 Neural Computation Principal Component Analysis
The following principles provide the neurobiological basis for the adaptive algorithms for principal component analysis: Some reduction. Let the data vector a feature space that is

http://www.cs.tut.fi/~avisa/2806nn8.ppt

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Factor and Component Analysis
… dimension reduction ; Principal Component Analysis . Feature Extraction in ECG data (Raw Data) Feature Extraction in ECG data (PCA)

http://www.cs.princeton.edu/picasso/mats/Lecture1_jps.ppt

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Data Mining: Concepts and Techniques
Feature subset selection, feature creation; Numerosity reduction (some simply call it: Data Reduction) Regression Principal Component Analysis (Steps)

http://www.cs.uiuc.edu/homes/hanj/cs412/bk3_slides/03Preprocessing.ppt

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Principal Component Analysis Based on L1-Norm Maximization
In data analysis problems, why do we need dimensionality reduction? Principal Component Analysis(PCA) PCA we can compute the variance of the feature. The

http://vc.cs.nthu.edu.tw/home/paper/codfiles/tychiu/200808151557/Principal_Component_Analysis_Based_on_L1-Norm_Maximization.ppt

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投影片 1
Principal Component Analysis (PCA) They, hence, form a basis of the feature space. For dimensionality reduction, only choose few of them.

http://aimm02.cse.ttu.edu.tw/class_2009_1/PR/Lecture%25205/Feature%2520Extraction.ppt

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Neural Networks Part 2
This is not a feature selection method, but a feature reduction method. 17 . x 1 . x 2 . Class 1 . Principal Component Analysis (PCA) We are given input vectors x n

http://academic.csuohio.edu/simond/courses/eec645/NeuralNets2.ppt

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Data Mining for Analysis of Rare Events: A Case of Computer
Principal component analysis (PCA) Very popular, well-established, frequently used; Feature Reduction Methods . InfoSec Seminar, September 27, 2005 .

http://louisville.edu/infosec/Fall%25202005/Zurada.ppt

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