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Erschienen in:
Sparse Principal Component Analysis via Rotation and Truncation Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Principal Component Analysis for Exponential Family Data

verfasst von : Meng Lu, Kai He, Jianhua Z. Huang, Xiaoning Qian

Erschienen in: Advances in Principal Component Analysis

Verlag: Springer Singapore

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Abstract

This chapter reviews exponential family principal component analysis (ePCA), a family of statistical methods for dimension reduction of large-scale data that are not real-valued, such as user ratings for items in e-commerce, categorical/count genetic data in bioinformatics, and digital images in computer vision. The ePCA framework extends the applications of traditional PCA to modern data containing various data types. A sparse version of ePCA further helps overcome the model inconsistency and improve interpretability when applied to high-dimensional data. Model formulations and solution strategies of ePCA and sparse ePCA are discussed with real-world applications.

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Vorheriges Kapitel Damage and Fault Detection of Structures Using Principal Component Analysis and Hypothesis Testing
Nächstes Kapitel Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability
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Metadaten
Titel
Principal Component Analysis for Exponential Family Data
verfasst von
Meng Lu
Kai He
Jianhua Z. Huang
Xiaoning Qian
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Advances in Principal Component Analysis

Print ISBN: 978-981-10-6703-7

Electronic ISBN: 978-981-10-6704-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-6704-4_8

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