Deep learning through sparse and low-rank modeling / edited by Zhangyang Wang, Yun Fu, Thomas S. Huang.
| Other author | Wang, Zhangyang. |
| Other author | Fu, Yun. |
| Other author | Huang, Thomas S., 1936- |
| Format | Electronic |
| Publication Info | London ; San Diego : Academic Press, an imprint of Elsevier, [2019] |
| Description | xvii, 277 pages ; 24 cm |
| Supplemental Content | Full text available from eBook - Engineering 2019 |
| Subjects |
| Series | Computer vision and pattern recognition series Computer vision and pattern recognition series. ^A1361494 |
| Abstract | Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2021277362 |
| ISBN | 9780128136591 paperback |
| ISBN | 0128136596 paperback |
| ISBN | electronic book |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |