Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications [Scanning the Issue]

The papers in this special issue introduce the reader to the theory, algorithms, and applications of principal component analysis (PCA) and its many extensions. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 106; no. 8; pp. 1274 - 1276
Main Authors: Vaswani, Namrata, Chi, Yuejie, Bouwmans, Thierry
Format: Journal Article Publication
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algorithm design and analysis
Algorithms
Clustering
Computer Science
Computer vision
Data analysis
Domains
Fault detection
Image Processing
Medical imaging
Multivariate analysis
Principal component analysis
Principal components analysis
Recommender systems
Special issues and sections
Statistical analysis
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Summary:The papers in this special issue introduce the reader to the theory, algorithms, and applications of principal component analysis (PCA) and its many extensions. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is often the first step in various types of exploratory data analysis, predictive modeling, and classification and clustering tasks, and finds applications in biomedical imaging, computer vision, process fault detection, recommendation systems’ design, and many more domains.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2853498