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Advances in Principal Component Analysis

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Cover of 'Advances in Principal Component Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Sparse Principal Component Analysis via Rotation and Truncation
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    Chapter 2 PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images
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    Chapter 3 Principal Component Analysis in the Presence of Missing Data
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    Chapter 4 Robust PCAs and PCA Using Generalized Mean
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    Chapter 5 Principal Component Analysis Techniques for Visualization of Volumetric Data
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    Chapter 6 Outlier-Resistant Data Processing with L1-Norm Principal Component Analysis
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    Chapter 7 Damage and Fault Detection of Structures Using Principal Component Analysis and Hypothesis Testing
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    Chapter 8 Principal Component Analysis for Exponential Family Data
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    Chapter 9 Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability
Attention for Chapter 1: Sparse Principal Component Analysis via Rotation and Truncation
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