By Irina Rish,Genady Grabarnik
Sparse versions are rather priceless in medical purposes, similar to biomarker discovery in genetic or neuroimaging info, the place the interpretability of a predictive version is key. Sparsity may also dramatically enhance the fee potency of sign processing.
Sparse Modeling: thought, Algorithms, and Applications offers an creation to the turning out to be box of sparse modeling, together with software examples, challenge formulations that yield sparse options, algorithms for locating such options, and up to date theoretical effects on sparse restoration. The booklet will get you up to the mark at the most up-to-date sparsity-related advancements and should inspire you to continue to learn concerning the field.
The authors first current motivating examples and a high-level survey of key fresh advancements in sparse modeling. The ebook then describes optimization difficulties related to everyday sparsity-enforcing instruments, offers crucial theoretical effects, and discusses numerous cutting-edge algorithms for locating sparse strategies.
The authors cross directly to deal with a number of sparse restoration difficulties that stretch the fundamental formula to extra subtle kinds of based sparsity and to assorted loss features. additionally they learn a specific type of sparse graphical versions and canopy dictionary studying and sparse matrix factorizations.
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Sparse Modeling: Theory, Algorithms, and Applications (Chapman & Hall/Crc Machine Learning & Pattern Recognition) by Irina Rish,Genady Grabarnik
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