Independent Component Analysis: A Tutorial Introduction (MIT Press)

James V. Stone

3,75 valoración promedio
( 12 valoraciones por Goodreads )

A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples.

Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.

In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.

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From the Inside Flap:

"Independent component analysis is a recent and powerful addition to the methods that scientists and engineers have available to explore large data sets in high-dimensional spaces. This book is a clearly written introduction to the foundations of ICA and the practical issues that arise in applying it to a wide range of problems."
--Terrence J. Sejnowski, Howard Hughes Medical Institute, Salk Institute for Biological Studies, and University of California, San Diego

"This monograph provides a delightful tour, through the foothills of linear algebra to the higher echelons of independent components analysis, in a graceful and deceptively simple way. Its careful construction, introducing concepts as they are needed, discloses the fundamentals of source separation in a remarkably accessible and comprehensive fashion."
--Karl J. Friston, University College London

"This fantastic book provides a broad introduction to both the theory and applications of independent component analysis. I recommend it to any student interested in exploring this emerging field."
--Martin J. McKeown, Associate Professor of Medicine (Neurology), University of British Columbia

James V. Stone is a Reader in the Psychology Department of the University of Sheffield. He is coauthor (with John P. Frisby) of the widely used text Seeing: The Computational Approach to Biological Vision (second edition, MIT Press, 2010), and author of Independent Component Analysis: A Tutorial Introduction (MIT Press, 2004).

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1.Independent Component Analysis: A Tutorial Introduction (Paperback)

Editorial: MIT Press Ltd, United States (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158
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The Book Depository
(London, Reino Unido)
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Descripción MIT Press Ltd, United States, 2004. Paperback. Estado de conservación: New. Language: English . Brand New Book. Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls the mathematical nuts and bolts of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code. Nº de ref. de la librería AAZ9780262693158

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2.Independent Component Analysis

Editorial: MIT Press (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158
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Descripción MIT Press, 2004. PAP. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería WM-9780262693158

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3.Independent Component Analysis: A Tutorial Introduction (Paperback)

Editorial: MIT Press Ltd, United States (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158
Librería
The Book Depository US
(London, Reino Unido)
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Descripción MIT Press Ltd, United States, 2004. Paperback. Estado de conservación: New. Language: English . Brand New Book. A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples.Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls the mathematical nuts and bolts of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code. Nº de ref. de la librería AAZ9780262693158

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4.Independent Component Analysis: A Tutorial Introduction (Bradford Books) (Paperback)

ISBN 10: 0262693151 ISBN 13: 9780262693158
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Descripción Estado de conservación: New. Bookseller Inventory # ST0262693151. Nº de ref. de la librería ST0262693151

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5.Independent Component Analysis

Editorial: MIT Press 2004-10-15 (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158
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Descripción MIT Press 2004-10-15, 2004. Estado de conservación: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. de la librería NU-GRD-04729614

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6.Independent Component Analysis

Editorial: MIT Press 2004-10-15, Cambridge, Mass. |London (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158
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Descripción MIT Press 2004-10-15, Cambridge, Mass. |London, 2004. paperback. Estado de conservación: New. Nº de ref. de la librería 9780262693158

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7.Independent Component Analysis: A Tutorial Introduction (Bradford Books) Format: Paperback

Editorial: MIT Press
ISBN 10: 0262693151 ISBN 13: 9780262693158
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8.Independent Component Analysis: A Tutorial Introduction

ISBN 10: 0262693151 ISBN 13: 9780262693158
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10.Independent Component Analysis

Editorial: MIT Press Ltd (2004)
ISBN 10: 0262693151 ISBN 13: 9780262693158