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: Deep Learning Through Sparse and Low-Rank Modeling
: Zhangyang Wang, Yun Fu, Thomas S Huang
: Academic Press
ISBN: 9780128136591
: 2019
: 281
:
: pdf (true)
: 17.8 MB

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.

Machine learning makes computers learn from data without explicitly programming them. However, classical machine learning algorithms often find it challenging to extract semantic features directly from raw data, e.g., due to the well-known semantic gap, which calls for the assistance from domain experts to hand-craft many well-engineered feature representations, on which the machine learning models operate more effectively.

In contrast, the recently popular deep learning relies on multilayer neural networks to derive semantically meaningful representations, by building multiple simple features to represent a sophisticated concept. Deep learning requires less hand-engineered features and expert knowledge.

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 and Low-Rank Modeling








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