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Multilinear principal component analysis (MPCA) is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multidimensional objects of lower dimensions. There is one orthogonal (linear) transformation for each dimension (mode); hence multilinear. This transformation aims to capture as high a variance as possible, accounting for as much of the variability in the data as possible, subject to the constraint of mode-wise orthogonality.

MPCA is a multilinear extension of principal component analysis (PCA). The major difference is that PCA needs to reshape a multidimensional object into a vector, while MPCA operates directly on multidimensional objects through mode-wise

MPCA is a multilinear extension of principal component analysis (PCA). The major difference is that PCA needs to reshape a multidimensional object into a vector, while MPCA operates directly on multidimensional objects through mode-wise

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## Nt1310 Unit 4 Lab Report

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## Nt1310 Unit 1 Exercise 1

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## Pt1420 Unit 6

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## Nt1310 Unit 4 Test Paper

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## Nt1310 Unit 3 Ggression Analysis

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## Nt1310 Unit 6 Lab 1

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## NT1330 Unit 3 Assignment 1

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## Data Encryption Research Paper

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## Math 1280 Unit 4 Assignment

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## Nt1310 Unit 4 Test Lab Report

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## Nt1330 Unit 1 Program Analysis

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