'Principal component analysis pca' diaporamas de présentation

Factor and Component Analysis

Factor and Component Analysis. esp. Principal Component Analysis (PCA). Why Factor or Component Analysis?. We study phenomena that can not be directly observed ego, personality, intelligence in psychology Underlying factors that govern the observed data

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Multivariate Analysis And PCA

Multivariate Analysis And PCA. Principal Components Analysis ( PCA). Is a Factor Analytic method Can be used to: Reduce number of dimensions in data Find patterns in high-dimensional data Visualize data of high dimensionality Example applications: Face recognition Image compression

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Face Recognition and Feature Subspaces

03/09/10. Face Recognition and Feature Subspaces. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Some slides from Lana Lazebnik, Silvio Savarese, Fei-Fei Li. Object recognition. Last Class Object instance recognition: focus on localization of miscellaneous objects

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Face Recognition and Feature Subspaces

03/10/11. Face Recognition and Feature Subspaces. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Presented by Ali Farhadi. Some slides from Lana Lazebnik, Silvio Savarese, Fei-Fei Li. Object recognition. Last Class

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Analyzing Patterns Using Principal Component Analysis

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[Start] Generate random population of N chromosomes (suitable solutions for the problem)

[Start] Generate random population of N chromosomes (suitable solutions for the problem) [Fitness] Evaluate the fitness f(x) of each chromosome x in the population [New population] Create a new population by repeating following steps until the new population is complete

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LE THAI HOA Vietnam National University, Hanoi

Open Seminar at Tokyo Polytechnic University . POD AND NEW INSIGHTS IN WIND ENGINEERING . LE THAI HOA Vietnam National University, Hanoi . PART 1. CONTENTS .

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CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision. Pattern Recognition. Computer vision: related topics. Pattern recognition. One of the leading vision conference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pattern recognition and machine learning

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More QSAR

More QSAR. QSAR equations form a quantitative connection between chemical structure and (biological) activity. Problems: Which descriptors to use How to test/validate QSAR equations (continued from lecture 5). Evaluating QSAR equations (I).

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A. Das , S. Misra, S. Joshi*, J. Zambreno + , G. Memik, and A. Choudhary

Design, Automation & Test in Europe (DATE) 2008. An Efficient FPGA Implementation of Principal Component Analysis based Network Intrusion Detection System. A. Das , S. Misra, S. Joshi*, J. Zambreno + , G. Memik, and A. Choudhary Electrical Engineering and Computer Science Department.

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Detecting Large-Scale System Problems by Mining Console Logs

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Curvilinear Distance Analysis versus Isomap.

Curvilinear Distance Analysis versus Isomap. Presented By: Avadhanula.Laxminarayana kumar. Venishetty Kalyan Kumar. Motivation. Comparing two nonlinear projection methods.

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AIRUS (Automatic Information Retrieval Using Speech)

AIRUS (Automatic Information Retrieval Using Speech). Winter School `09-> Speech Track -> Group 4 Pulkit Shantanu Nitish Abhilash. Problem Formulation. N people are discussing in a group, each has a microphone Aim is to Remove the crosstalk in the speeches recorded by each microphone.

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Tobias Kind FiehnLab at UC Davis Genome Center November 2006

Statistics evaluation and graphics with ChemAxon tools and Statistica and WEKA towards QSPR and QSAR development. Tobias Kind FiehnLab at UC Davis Genome Center November 2006. Free Academic Licenses for JChem and Instant JChem provided by ChemAxon.

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Face Recognition Committee Machine

Face Recognition Committee Machine. Presented by Sunny Tang. Outline. Motivation Algorithms Review Face Recognition Committee Machine (FRCM) Experimental Results Conclusion and Future Work. Motivation. Applications in security Authentication Identification

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Genomics, Proteomics and Metabolomics

Genomics, Proteomics and Metabolomics. Genomics. The complete set of DNA found in each cell is known as the genome Most crop plant genomes have billions of nucleotide bases Arabidopsis thaliana has 120 million bases that encode approximately 25,000 genes

1.49k views • 19 slides

PARSEC vs. SPLASH-2 :

PARSEC vs. SPLASH-2 :. A Quantitative Comparison of Two Multithreaded Benchmark Suites on Chip-Multiprocessors. ??? ???. INTRODUCTION (1/2). Princeton Application Repository for Shared-Memory Computers What distinguishes PARSEC from other benchmark suites?

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Principal component analysis (PCA)

Principal component analysis (PCA). Purpose of PCA Covariance and correlation matrices PCA using eigenvalues PCA using singular value decompositions Selection of variables Biplots References. Purpose of PCA.

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The Discrete Wavelet Transform

Location of Partial Discharges within a Transformer Winding Using Principal Component Analysis M. S. Abd Rahman, L. Hao, P. L. Lewin University of Southampton, Southampton, UK. PD Source. Introduction.

180 views • 1 slides