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2327 results in Pattern Recognition and Machine Learning

Page 41 of 117

High-Dimensional Data Analysis with Low-Dimensional Models
  • Principles, Computation, and Applications
  • John Wright, Yi Ma
  • Published online:
    11 March 2022
    Print publication:
    13 January 2022
  • Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

  • 3 - Fundamental Theory and Algorithms of Edge Learning

  • Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
  • Book:
    Edge Learning for Distributed Big Data Analytics
    Published online:
    14 January 2022
    Print publication:
    10 February 2022, pp 24-41

  • Summary

    In this chapter, we first provide convergence results of Stochastic Gradient Descent (SGD) methods that are usually adopted to solve the machine learning problem. Then, we introduce advanced training algorithms including momentum SGD, Hyper-parameter-based algorithms, and optimization algorithms for deep learning models. At last, we give theoretical frameworks about how to deal with the staleness gradient incurred by ASP or SSP.

  • 5 - Computation Acceleration

  • Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
  • Book:
    Edge Learning for Distributed Big Data Analytics
    Published online:
    14 January 2022
    Print publication:
    10 February 2022, pp 73-97

  • Summary

    This chapter first focuses on model compression and hardware acceleration for edge learning. It covers many aspects, including the learning algorithms, learning-oriented communication, distributed machine learning with hardware adaptation, TEE-based privacy protection, algorithm, and hardware joint optimization, etc. The essential objective is to implement an integrated algorithm-hardware platform, to optimize the implementation of emerging machine learning algorithms, to fully explore the potential of modern computation hardware, and to promote novel intelligent applications for sophisticated services. Then, we introduce straggler tolerance schemes that can avoid the overall training performance seriously degraded by faulty nodes, and can adequately utilize the computation power of slow nodes. At last, we introduce computation acceleration technologies for inference at the edge.

Page 41 of 117