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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.
This book provides a broader introduction to the theories and
applications of sparse coding techniques in computer vision
research. It introduces sparse coding in the context of
representation learning, illustrates the fundamental concepts, and
summarizes the most active research directions. A variety of
applications of sparse coding are discussed, ranging from low-level
image processing tasks such as super-resolution and de-blurring to
high-level semantic understanding tasks such as image recognition,
clustering and fusion.The book is suitable to be used as an
introductory overview to this field, with its theoretical part
being both easy and precious enough for quick understanding. It is
also of great value to experienced researchers as it offers new
perspective to the underlying mechanism of sparse coding, and
points out potential future directions for different applications.
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