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Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities b
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- ISBN-13
- 9781536139945
- Book Title
- Independent Component Analysis (ICA)
- ISBN
- 9781536139945
- Series
- Computer Science, Technology and Applications Ser.
- Publication Year
- 2018
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Publication Name
- Independent Component Analysis (Ica) : Algorithms, Applications and Ambiguities
- Item Length
- 9.1in
- Publisher
- NOVA Science Publishers, Incorporated
- Item Width
- 6.1in
- Item Weight
- 20.6 Oz
- Number of Pages
- 254 Pages
關於產品
Product Information
Modern treatment of data requires powerful tools that allow the possible valuable contents of that data to be thoroughly understood and exploited. From the plethora of techniques proposed to achieve those objectives, the independent component analysis (ICA) has emerged as a flexible and efficient approach to model and characterize arbitrary data densities. Considering adequate data preprocessing, ICA can be implemented for any kind of data including imaging; biomedical signals; telecommunication data; and web data. In this framework, this book embraces a significant vision of ICA that presents innovative theoretical and practical approaches. ICA has been increasingly studied as a suitable method for many applications where available data describe complex geometries. Thus, this book aims to be an updated and advanced source of knowledge to solve real-world problems efficiently based on ICA. In contrast to classical time and frequency domain filtering, ICA has been proposed as a statistical filtering tool considering the observed data as mixtures of hidden non-Gaussian distributions called sources. Those sources extracted by ICA can be related with meaningful information about the origin of the data and for data detection/classification. Therefore, the successful of ICA has been widely demonstrated in challenging blind source separation (BSS), feature extraction, and pattern recognition tasks. The suitability of ICA for a given problem of data analysis can be posed from different perspectives considering the physical interpretation of the phenomenon under analysis: (i) Estimation of the probability density of multivariate data without physical meaning; (ii) learning of some bases (usually called activation functions), which are more or less connected to the actual behaviors that are implicit in the physical phenomenon; and (iii) to identify where sources are originated and how they mix before arriving to the sensors to provide a physical explanation of the linear mixture model. In any case, even though the complexity of the problem constrains a physical interpretation, ICA can be used as a general-purpose data mining technique. The chapters that compose this book are written by premier researchers that present enlightening discussions, convincing demonstrations, and guidelines for future directions of research. The contents of this book span biomedical signal processing, dynamic modeling, next generation wireless communication, and sound and ultrasound signal processing. It also includes comprehensive works based on the related ICA techniques known as bounded component analysis (BCA) and non-negative matrix factorization (NMF).
Product Identifiers
Publisher
NOVA Science Publishers, Incorporated
ISBN-10
1536139947
ISBN-13
9781536139945
eBay Product ID (ePID)
27038369456
Product Key Features
Publication Name
Independent Component Analysis (Ica) : Algorithms, Applications and Ambiguities
Format
Hardcover
Language
English
Series
Computer Science, Technology and Applications Ser.
Publication Year
2018
Type
Textbook
Number of Pages
254 Pages
Dimensions
Item Length
9.1in
Item Width
6.1in
Item Weight
20.6 Oz
Additional Product Features
Lc Classification Number
Qa278.I46 2017
Table of Content
For more information, please visit our website at:Hardcover: https://www.novapublishers.com/catalog/product_info.php'products_id=65002E-book: https://www.novapublishers.com/catalog/product_info.php'products_id=65003
Copyright Date
2018
Topic
Probability & Statistics / Multivariate Analysis, Finite Mathematics
Lccn
2018-036961
Dewey Decimal
621.382/2
Dewey Edition
23
Illustrated
Yes
Genre
Mathematics
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