Compressed Sensing and Sparse Filtering PDF ePub eBook

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Compressed Sensing and Sparse Filtering free pdf This book is aimed at presenting fundamental concepts, methods and algorithms able to cope with undersampled data. One such trend that recently gained popularity and to some extent revolutionized signal processing is compressive sampling, called also compressive sensing. Apart from compressed sensing, this book introduces the readers to other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Analogously, stochastic systems may use point processes for dynamic variable selection, thereby relaxing the need for large data sets. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilize L1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. Sparse manifold learning and constrained state filtering are other examples of interest. The book emphasizes on the role of sparsity and its connections to model and variable selection in various engineering problems, and likewise to dimensionality reduction and manifold learning.

About Avishy Y. Carmi

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Details Book

Author : Avishy Y. Carmi
Publisher : Springer-Verlag Berlin and Heidelberg GmbH
Data Published : 30 June 2013
ISBN : 3642383971
EAN : 9783642383977
Format Book : PDF, Epub, DOCx, TXT
Number of Pages : 526 pages
Age + : 15 years
Language : English
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