Deep Learning through Sparse and Low-Rank Modeling
- 1st Edition - April 11, 2019
- Latest edition
- Authors: Zhangyang Wang, Yu Fu, Thomas S. Huang
- Language: English
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent de… Read more
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Description
Description
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.
Key features
Key features
- Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks
- Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models
- Provides tactics on how to build and apply customized deep learning models for various applications
Readership
Readership
Researchers and graduate students in computer vision, machine learning, signal processing, optimization, and statistics
Table of contents
Table of contents
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 12, 2019
- Language: English
About the authors
About the authors
ZW
Zhangyang Wang
YF
Yu Fu
Prof. Yu Fu is a Full Professor at College of Food Science, Southwest University, China. He earned his PhD degree in Food Science from Aarhus University and completed postdoctoral research at University of Copenhagen. He has also served as a visiting scholar at the University of Manitoba and the University of Aberdeen. Dr. Fu has led several research projects, including National Natural Science Foundation grants, National Key R&D Program, etc. He has published over 100 peer-reviewed papers as first or corresponding author in internationally respected journals such as Journal of Advanced Research, Trends in Food Science & Technology, Journal of Agricultural and Food Chemistry, and Food Chemistry. He has contributed to eight English academic books and holds ten national invention patents. He also serves the scientific community in numerous professional and editorial roles, including member of Youth Working Committee of Chinese Association of Animal Products Processing, expert of Technical Advisory Committee of Chongqing Agricultural Product Processing Association, Editor-in-Chief of International Journal of Food Studies, Editor for Trends in Food Science & Technology, Academic Editor for Journal of Food Biochemistry, Deputy Editor for International Journal of Food Science & Technology. He was listed among Elsevier’s “Top 2% Scientists” (2023–2025) and has received awards including the ACU Early Career Award, Foods Outstanding Young Scholar Award, Excellent Instructor Award for International College Students’ Innovation Competition (Gold award), EFFoST “PhD Student of the Year” award, and the Best Oral Presentation Award at the ICoMST.
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