Dictionary Learning Algorithms and Applications

  • Bogdan Dumitrescu
  • Paul Irofti

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Bogdan Dumitrescu, Paul Irofti
    Pages 1-23
  3. Bogdan Dumitrescu, Paul Irofti
    Pages 25-43
  4. Bogdan Dumitrescu, Paul Irofti
    Pages 45-87
  5. Bogdan Dumitrescu, Paul Irofti
    Pages 89-113
  6. Bogdan Dumitrescu, Paul Irofti
    Pages 115-144
  7. Bogdan Dumitrescu, Paul Irofti
    Pages 145-165
  8. Bogdan Dumitrescu, Paul Irofti
    Pages 167-208
  9. Bogdan Dumitrescu, Paul Irofti
    Pages 209-229
  10. Bogdan Dumitrescu, Paul Irofti
    Pages 231-255
  11. Bogdan Dumitrescu, Paul Irofti
    Pages 257-269
  12. Back Matter
    Pages 271-284

About this book


This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that 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, including shift invariant; orthogonal blocks or factored dictionaries; and 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.


Dictionary Learning and Signal Processing Dictionary Learning in Visual Computing Dictionary Learning Algorithms Dictionary Methods Learning Problem Variations

Authors and affiliations

  • Bogdan Dumitrescu
    • 1
  • Paul Irofti
    • 2
  1. 1.Department of Automatic Control and Systems Engineering, Faculty of Automatic Control and ComputersUniversity Politehnica of BucharestBucharestRomania
  2. 2.Department of Computer Science, Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-78673-5
  • Online ISBN 978-3-319-78674-2
  • Buy this book on publisher's site
Industry Sectors
IT & Software
Oil, Gas & Geosciences