Tutorial on Principal components analysis (PCA)

Nov 25, 2015

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Tutorial on principal components analysis or PCA. Requires knowledge of statistics and linear algebra, in particular, knowing how to calculate the covariance matrix and what eigenvalues and eigenvectors are.

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Tutorial on Principal components analysis (PCA)

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Principal Components Analysis with SAS

When to Use PCA. You have a set of p continuous variables.You want to repackage their variance into m components.You will usually want m to be < p, but not always.. Components and Variables. Each component is a weighted linear combination of the variablesEach variable is a weighted linear combination of the components..

1.01k views • 41 slides

Principal Components Analysis with SPSS

Principal Components Analysis with SPSS. Karl L. Wuensch Dept of Psychology East Carolina University. When to Use PCA. You have a set of p continuous variables. You want to repackage their variance into m components. You will usually want m to be < p , but not always.

886 views • 52 slides

Principal Components Analysis

Principal Components Analysis. Hal Whitehead BIOL4062/5062. Principal Components Analysis {PCA}. A. K. A.: latent vectors latent variates principal axes principal factors etc. Principal Components Analysis: Principal purpose:. Reducing dimensionality:

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Reduksi Dimensi Image dengan Principal Components Analysis (PCA)

Reduksi Dimensi Image dengan Principal Components Analysis (PCA) . Sumber: Trucco & Verri chap. 10 Standford Vision & Modeling. Contoh: problem Pattern Recognition. Rotate coordinate system:. Problem Dimensi tinggi ??. PCA (Principal Component Analysis).

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Principal Components Analysis ( PCA)

Principal Components Analysis ( PCA). An exploratory technique used to reduce the dimensionality of the data set to 2D or 3D Can be used to: Reduce number of dimensions in data Find patterns in high-dimensional data Visualize data of high dimensionality Example applications:

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Principal Components Analysis (PCA)

Principal Components Analysis (PCA). Principal Components Analysis (PCA). a technique for finding patterns in data of high dimension. Outline: Eigenvectors and eigenvalues PCA: Getting the data Centering the data Obtaining the covariance matrix

578 views • 39 slides

Principal Components Analysis with SAS

Principal Components Analysis with SAS. Karl L. Wuensch Dept of Psychology East Carolina University. When to Use PCA. You have a set of p continuous variables. You want to repackage their variance into m components. You will usually want m to be < p , but not always.

865 views • 41 slides

4 Principal Component Analysis (PCA)

4 Principal Component Analysis (PCA). Principal Component Analysis (PCA). PCA ✔ PCA in high dimensional space Nonlinear PCA Kernel PCA KL expansion 2D-PCA. 主分量. Dimension Reduction. 农作物. Dimension Reduction. 语言. Intuition. Intuition (Continued). Principal Component Analysis.

1.36k views • 86 slides

Principal Components Analysis

Principal Components Analysis. Objectives: Understand the principles of principal components analysis (PCA) Recognize conditions under which PCA may be useful Use SAS procedure PRINCOMP to perform a principal components analysis interpret PRINCOMP output. Typical Form of Data.

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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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Principal Components Analysis

Principal Components Analysis. Eric Vaagen, FCAS Assistant Actuary September 5, 2008. Agenda. Motivation What is PCA? Background Simple example Is PCA right for you?. Motivation. Forecast average premium by coverage Explanatory variables Vehicle use, territory, driving record

566 views • 25 slides

Principal Component Analysis (PCA)

Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA). Principal Component Analysis (PCA).

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Principal Components Analysis (PCA)

Principal Components Analysis (PCA). PCA Goals. Given N “experiments” each determining a set of values for M data variables, where M is large Find The “best” set of L variables, L < M, that describe this data

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PRINCIPAL COMPONENTS ANALYSIS (PCA) Eigenanalysis (Hotelling, 1930’s)

PRINCIPAL COMPONENTS ANALYSIS (PCA) Eigenanalysis (Hotelling, 1930’s). What is it?. PCA allows to explore the relations between multiple variables at the same time and to extract the fundamental structure of the data cloud;

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Principal components

Principal components. Model and concept No Y’s, no model. Partition, with maximum separation, of total variance into orthogonal components. Assumptions and screening Geometry of concept Procedure and analysis Potential problems. Assumptions for PCA (1). Normality.

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Principal Components and Factor Analysis

Principal Components and Factor Analysis. Dr. Adel Elomri adel.elomri@qu.edu.qa. Principal Components Analysis (PCA). Data Analysis and Presentation. We have too many observations and dimensions To reason about or obtain insights from To visualize

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Principal component analysis (PCA)

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Principal Components Analysis

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Principal Components Analysis ( PCA)

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Principal Components Analysis

Principal Components Analysis. Jan Sobotka ( sobotj 1@fel.cvut.cz). PCA Algorithm. Statistical technique PCA was invented in 1901 by Karl Pearson Is a technique used to reduce multidimensional data sets to lower dimensions

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