Deep Learning Through Sparse and Low-Rank Modeling (pdf)

Description

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.

• 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 applicationsDeep 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.