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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the IEEE Ročník 106; číslo 8; s. 1274 - 1276
Hlavní autoři: Vaswani, Namrata, Chi, Yuejie, Bouwmans, Thierry
Médium: Journal Article Publikace
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2853498