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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

A Simple Review of Sparse Principal Components Analysis

verfasst von : Chun-Mei Feng, Ying-Lian Gao, Jin-Xing Liu, Chun-Hou Zheng, Sheng-Jun Li, Dong Wang

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

Principal Component Analysis (PCA) is a common tool for dimensionality reduction and feature extraction, which has been applied in many fields, such as biology, medicine, machine learning and bioinformatics. But PCA has two obvious drawbacks: each principal component is line combination and loadings are non-zero which is hard to interpret. Sparse Principal Component Analysis (SPCA) was proposed to overcome these two disadvantages of PCA under the circumstances. This review paper will mainly focus on the research about SPCA, where the basic models of PCA and SPCA, various algorithms and extensions of SPCA are summarized. According to the difference of objective function and the constraint conditions, SPCA can be divided into three groups as it shown in Fig. 1. We also make a comparison among the different kind of sparse penalties. Besides, brief statements and other different classifications are summarized at last.

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Vorheriges Kapitel Robust Epileptic Seizure Classification
Nächstes Kapitel Endpoint Detection and De-noising Method Based on Multi-resolution Spectrogram
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Metadaten
Titel
A Simple Review of Sparse Principal Components Analysis
verfasst von
Chun-Mei Feng
Ying-Lian Gao
Jin-Xing Liu
Chun-Hou Zheng
Sheng-Jun Li
Dong Wang
Copyright-Jahr
2016
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-42293-0

Electronic ISBN: 978-3-319-42294-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42294-7_33