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Deep Learning Through Sparse and Low Rank Modeling 1st Edition by Zhangyang Wang, Yun Raymond, Thomas S Huang ISBN 0128136596 9780128136591

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Deep Learning Through Sparse and Low Rank Modeling Zhangyang Wang Digital Instant Download

Author(s): Zhangyang Wang; Yun Raymond; Thomas S Huang
ISBN(s): 9780128136591, 0128136596
Edition: Paperback
File Details: PDF, 17.79 MB
Year: 2019
Language: english
SKU: EB-9968724 Categories: , Tags: , ,

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ISBN 10: 0128136596 
ISBN 13: 9780128136591
Author: Zhangyang Wang, Yun Raymond, Thomas S Huang

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.

  • 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

Deep Learning Through Sparse and Low Rank Modeling 1st Table of contents:

Chapter 1: Introduction

  • Abstract

  • 1.1. Basics of Deep Learning

  • 1.2. Basics of Sparsity and Low-Rankness

  • 1.3. Connecting Deep Learning to Sparsity and Low-Rankness

  • 1.4. Organization

  • References

Chapter 2: Bi-Level Sparse Coding: A Hyperspectral Image Classification Example

  • Abstract

  • 2.1. Introduction

  • 2.2. Formulation and Algorithm

  • 2.3. Experiments

  • 2.4. Conclusion

  • 2.5. Appendix

  • References

Chapter 3: Deep ℓ0 Encoders: A Model Unfolding Example

  • Abstract

  • 3.1. Introduction

  • 3.2. Related Work

  • 3.3. Deep ℓ0 Encoders

  • 3.4. Task-Driven Optimization

  • 3.5. Experiment

  • 3.6. Conclusions and Discussions on Theoretical Properties

  • References

Chapter 4: Single Image Super-Resolution: From Sparse Coding to Deep Learning

  • Abstract

  • 4.1. Robust Single Image Super-Resolution via Deep Networks with Sparse Prior

  • 4.2. Learning a Mixture of Deep Networks for Single Image Super-Resolution

  • References

Chapter 5: From Bi-Level Sparse Clustering to Deep Clustering

  • Abstract

  • 5.1. A Joint Optimization Framework of Sparse Coding and Discriminative Clustering

  • 5.2. Learning a Task-Specific Deep Architecture for Clustering

  • References

Chapter 6: Signal Processing

  • Abstract

  • 6.1. Deeply Optimized Compressive Sensing

  • 6.2. Deep Learning for Speech Denoising

  • References

Chapter 7: Dimensionality Reduction

  • Abstract

  • 7.1. Marginalized Denoising Dictionary Learning with Locality Constraint

  • 7.2. Learning a Deep ℓ∞ Encoder for Hashing

  • References

Chapter 8: Action Recognition

  • Abstract

  • 8.1. Deeply Learned View-Invariant Features for Cross-View Action Recognition

  • 8.2. Hybrid Neural Network for Action Recognition from Depth Cameras

  • 8.3. Summary

  • References

Chapter 9: Style Recognition and Kinship Understanding

  • Abstract

  • 9.1. Style Classification by Deep Learning

  • 9.2. Visual Kinship Understanding

  • 9.3. Research Challenges and Future Works

  • References

Chapter 10: Image Dehazing: Improved Techniques

  • Abstract

  • 10.1. Introduction

  • 10.2. Review and Task Description

  • 10.3. Task 1: Dehazing as Restoration

  • 10.4. Task 2: Dehazing for Detection

  • 10.5. Conclusion

  • References

Chapter 11: Biomedical Image Analytics: Automated Lung Cancer Diagnosis

  • Abstract

  • Acknowledgements

  • 11.1. Introduction

  • 11.2. Related Work

  • 11.3. Methodology

  • 11.4. Experiments

  • 11.5. Conclusion

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Tags: Zhangyang Wang, Yun Raymond, Thomas S Huang, Deep Learning, Low Rank