Dictionary Learning Algorithms and Applications

Autor: Bogdan Dumitrescu, Paul Irofti

Język:

Oprawa: Twarda

Wydawca: Springer International Publishing AG

Dostępność: Dostępna u dostawcy

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634.67 zł

This book covers all the relevant dictionary learning algorithms, presenting them in full detail and...

Informacje o książce

Autor

Bogdan Dumitrescu, Paul Irofti

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2018

strony

284

EAN

9783319786735

ISBN

3319786733

Enbook ID

19042344

Wydawca

Springer International Publishing AG

Waga

617

Wymiary

155 x 235 x 23

Pełny opis

This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics but also revealing the similarities. It gives implementation tricks that are often ignored but are crucial for a successful program. Besides MOD, K-SVD and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, like shift invariant; orthogonal blocks or factored dictionaries; separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.

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