Low Rank Approximation

Algorithms, Implementation, Applications

Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Technology, Automation
Cover of the book Low Rank Approximation by Ivan Markovsky, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Ivan Markovsky ISBN: 9781447122272
Publisher: Springer London Publication: November 19, 2011
Imprint: Springer Language: English
Author: Ivan Markovsky
ISBN: 9781447122272
Publisher: Springer London
Publication: November 19, 2011
Imprint: Springer
Language: English

Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation; machine learning for information retrieval and clustering; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; and psychometrics for factor analysis.

Software implementation of the methods is given, making the theory directly applicable in practice. All numerical examples are included in demonstration files giving hands-on experience and exercises and MATLABĀ® examples assist in the assimilation of the theory.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation; machine learning for information retrieval and clustering; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; and psychometrics for factor analysis.

Software implementation of the methods is given, making the theory directly applicable in practice. All numerical examples are included in demonstration files giving hands-on experience and exercises and MATLABĀ® examples assist in the assimilation of the theory.

More books from Springer London

Cover of the book Software Engineering Frameworks for the Cloud Computing Paradigm by Ivan Markovsky
Cover of the book Difficult Decisions in Thoracic Surgery by Ivan Markovsky
Cover of the book Switchgrass by Ivan Markovsky
Cover of the book Metabonomics and Gut Microbiota in Nutrition and Disease by Ivan Markovsky
Cover of the book The Role of Micro-organisms in Non-infectious Diseases by Ivan Markovsky
Cover of the book Managing Common Interventional Radiology Complications by Ivan Markovsky
Cover of the book Kenaf: A Multi-Purpose Crop for Several Industrial Applications by Ivan Markovsky
Cover of the book Human Factors of Stereoscopic 3D Displays by Ivan Markovsky
Cover of the book Lower Abdominal and Perineal Surgery by Ivan Markovsky
Cover of the book Patterns, Programming and Everything by Ivan Markovsky
Cover of the book Global Energy Policy and Security by Ivan Markovsky
Cover of the book Compression Schemes for Mining Large Datasets by Ivan Markovsky
Cover of the book Clinical Trials in Rheumatology by Ivan Markovsky
Cover of the book Guide to ILDJIT by Ivan Markovsky
Cover of the book Calcium in Internal Medicine by Ivan Markovsky
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy