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.
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.
1. Introduction2. Bi-Level Sparse Coding: A Hyperspectral Image Classification Example3. Deep ℓ0 Encoders: AModel Unfolding Example4. Single Image Super-Resolution: FromSparse Coding to Deep Learning5. From Bi-Level Sparse Clustering to Deep Clustering6. Signal Processing7. Dimensionality Reduction8. Action Recognition9. Style Recognition and Kinship Understanding10. Image Dehazing: Improved Techniques11. Biomedical Image Analytics: Automated Lung Cancer Diagnosis
Dr. Zhangyang (Atlas) Wang is an Assistant Professor of Computer
Science and Engineering (CSE), at the Texas A&M University
(TAMU), since August 2017. During 2012-2016, he was a Ph.D. student
in the Electrical and Computer Engineering (ECE) Department, at the
University of Illinois at Urbana-Champaign (UIUC). He was a former
research intern with Microsoft Research (2015), Adobe Research
(2014), and US Army Research Lab (2013). Dr. Wang has published
over 70 papers in top-tier venues, in the broad fields of machine
learning, computer vision, artificial intelligence, and
interdisciplinary data science. He has published 2 books and 1
chapter, has been granted 3 patents, and has received over 20
research awards and scholarships. Dr. Wang regularly serves as
tutorial speakers, guest editors, area chairs, session chairs, TPC
members, and workshop organizers at leading conferences and
journals. Dr. Fu is an interdisciplinary faculty member affiliated
with College of Engineering and the College of Computer and
Information Science at Northeastern University. He received the
B.Eng. degree in information engineering and the M.Eng. degree in
pattern recognition and intelligence systems from Xi'an Jiaotong
University, China, respectively, and the M.S. degree in statistics
and the Ph.D. degree in electrical and computer engineering from
the University of Illinois at Urbana-Champaign, respectively.
Dr. Fu's research interests are Interdisciplinary research in
Machine Learning and Computational Intelligence, Social Media
Analytics, Human-Computer Interaction, and Cyber-Physical Systems.
He has extensive publications in leading journals, books/book
chapters and international conferences/workshops. Thomas S. Huang
received his B.S. Degree in Electrical Engineering from National
Taiwan University, Taipei, Taiwan, China; and his M.S. and Sc.D.
Degrees in Electrical Engineering from the Massachusetts Institute
of Technology, Cambridge, Massachusetts. He was on the Faculty of
the Department of Electrical Engineering at MIT from 1963 to 1973;
and on the Faculty of the School of Electrical Engineering and
Director of its Laboratory for Information and Signal Processing at
Purdue University from 1973 to 1980.
Dr. Huang's professional interests lie in the broad area of
information technology, especially the transmission and processing
of multidimensional signals. He has published 21 books, and over
600 papers in Network Theory, Digital Filtering, Image Processing,
and Computer Vision. Among his many honors and awards: Honda
Lifetime Achievement Award, IEEE Jack Kilby Signal Processing
Medal, and the King-Sun Fu Prize of the International Association
for Pattern Recognition.
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