Dictionary Learning Algorithms and Applications
Springer International Publishing (Verlag)
978-3-319-78673-5 (ISBN)
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.
Bogdan Dumitrescu received the M.S. and Ph.D. degrees in 1987 and 1993, respectively, from University Politehnica of Bucharest, Romania. He is a Professor with the Department of Automatic Control and Computers, University Politehnica of Bucharest. He held several visiting research positions at Tampere International Center for Signal Processing, Tampere University of Technology, Finland, in particular that of Finnish Distinguished Professor (FiDiPro) fellow (2010-2013). He was Associate Editor (2008-2012) and Area Editor (2010-2014) at IEEE Transactions on Signal Processing. He is the author of the book "Positive trigonometric polynomials and Signal Processing applications" (Springer 2007, second edition Springer 2017). His scientific interests are in optimization, numerical methods, and their applications to signal processing. Paul Irofti graduated from the University Politehnica of Bucharest in 2008 and received his PhD in 2016 with the thesis "Parallel Dictionary Learning Algorithms for Sparse Representations" from the same university. He is a Lecturer with the Department of Computer Science, University of Bucharest. His scientific interests are in numerical methods, signal processing and parallel algorithms accompanied by a vast, hands-on, more than 10 years' experience in the open-source community and industry. His publications are focused on dictionary learning algorithms.
Chapter1: Sparse representations.- Chapter2: Dictionary learning problem.- Chapter3: Standard algorithms.- Chapter4: Regularization and incoherence.- Chapter5: Other views on the DL problem.- Chapter6: Optimizing dictionary size.- Chapter7: Structured dictionaries.- Chapter8: Classification.- Chapter9: Kernel dictionary learning.- Chapter10: Cosparse representations.
| Erscheinungsdatum | 06.05.2018 |
|---|---|
| Zusatzinfo | XIV, 284 p. 48 illus., 47 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 613 g |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Schlagworte | Dictionary Learning Algorithms • Dictionary Learning and Signal Processing • Dictionary Learning in Visual Computing • Dictionary Methods • Learning Problem Variations |
| ISBN-10 | 3-319-78673-3 / 3319786733 |
| ISBN-13 | 978-3-319-78673-5 / 9783319786735 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
