Application Center - Maplesoft

# Principal Component Analysis

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Principal Component Analysis

Samir Khan

 Introduction Principal Component Analysis transforms a multi-dimensional data set to a new set of perpendicular axes (or components) that describe decreasing amounts of variance in the data.     This worksheet performs a principal component analysis on multi-dimensional data. Those components that have the least impact on the variance can be discarded, and the simplified data reconstructed from the remaining components.

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Specify Data

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Number of components to keep

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Centre the Data

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Calculate the Covariance, Eigvenvalues and Eigenvectors

Covariance Matrix

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Eigenvectors and eigenvalues

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Sorting the eigenvectors in order of decreasing eigenvalues (i.e. the most significant eigenvvectors are first)

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Calculate the Princpal Components

The feature contains the retained components

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Hence the data in terms of the new coordinate system.

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Reconstruct Data from Principal Components

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Plot Reconstruct Data

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