Machine Learning for Signal Analysis and System Modeling: Sparse and Event Driven Strategies

用于信号分析和系统建模的机器学习：稀疏和事件驱动策略

• 批准号: RGPIN-2017-05939
• 负责人: Pawlak, Miroslaw
• 金额: $ 1.89万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661776
• 关键词: Machine; Learning; Signal; Analysis; System

The proposed research is in the area of machine learning (ML) and its applications to signal processing and system modelling. ML is one the fastest growing fields of computer science, with far-reaching applications. These diverse applications demand for new ML algorithms able to cope with massive data sets of high dimensionality. In this proposal, I describe my plan to extend my current research on nonparametric/semiparametric learning methods to ML problems that are driven by two concepts: sparsity and events. ***This challenge includes not only the development of the new basic methodology, but also to verify it in the framework of signal and system analysis. Testing of the proposed methods in concrete applications within the areas of power engineering and biological signal processing is also planned. ***My research proposal relies on the idea of blending the modern nonparametric/semiparametric learning methodology with the concepts of sparsity and events. The sparsity approach to ML is based on the observation that real-world signals and systems are well characterized by a relatively small number of relevant parameters when compared to the dimension of their original space. Sparse modeling is deeply rooted in the ancient principle of parsimony and can be related to our daily lives. The key problem is to discover a sparse representation and its form in a given setting. The current research on ML with sparsity is mostly confined to finite-dimensional linear models. In this set-up the sparsity refers to the condition that most variables are close to zero. Efficient solutions for sparse linear models are based on the convex relaxation of the penalized least-squares criteria yielding the celebrated Lasso algorithm and its extensions. ***The event-driven approach can be viewed as a specific form of obtaining sparsity by generating a representation of the infinite-dimensional object based on observations that are acquired only when the event triggering condition holds. As a result, the object is represented by sparsely and randomly distributed instances. In signal analysis concrete event-based representations can be obtained from a sequence of events like level crossings and local extremes. Various event types may result in different sparse representations. Research is planned to select or combine various event schemes for the maximum accuracy and efficiency of the event-based learning.***The challenge of this proposal is to give thorough examination of the sparsity of infinite-dimensional objects generated by classical and event-based sparse representations. I believe that the proposed sparse and event driven ML paradigm can substantially enlarge a scope of ML applications and improve the existing algorithms within the context of examined case studies. Over this 5-year cycle I propose to train 6 PhD students (3 current + 3 new) and 3 MSc students (1 current + 2 new).

拟议的研究是在机器学习领域（ML）及其在信号处理和系统建模方面的应用。 ML是计算机科学发展最快的领域之一，具有深远的应用程序。 这些不同的应用程序需求对能够应对高维度的大量数据集的新ML算法。 在此提案中，我描述了我的计划，即将目前的非参数/半参数学习方法的研究扩展到由两个概念驱动的ML问题：稀疏性和事件。 ***这一挑战不仅包括新基本方法的开发，而且还包括在信号和系统分析框架中对其进行验证。还计划了在动力工程和生物信号处理领域内的混凝土应用中提出的方法的测试。 ***我的研究建议依赖于将现代非参数/半理学方法与稀疏和事件的概念融合的想法。 ML的稀疏方法基于这样的观察，即现实信号和系统与原始空间的尺寸相比，相对较少的相关参数的特征是相对较少的相关参数。 稀疏的建模深深植根于古老的简约原则，可能与我们的日常生活有关。 关键问题是在给定的环境中发现稀疏的表示及其形式。 当前对稀疏性ML的研究主要仅限于有限维线性模型。在此设置中，稀疏性是指大多数变量接近零的条件。稀疏线性模型的有效解决方案基于惩罚最小二乘标准的凸松弛，得出了著名的套索算法及其扩展。 ***事件驱动的方法可以通过基于观察值的观测值来产生无限维对对象的表示，将事件驱动的方法视为获得稀疏性的特定形式。结果，对象由稀疏和随机分布的实例表示。在信号分析中，基于混凝土事件的表示可以从一系列事件等事件等序列中获得。各种事件类型可能会导致不同的稀疏表示。计划选择或组合各种事件方案，以最大程度地和基于事件的学习的效率。我认为，提出的稀疏和事件驱动的ML范式可以大大扩大ML应用的范围，并在检查的案例研究的背景下改善现有算法。在这个5年的周期中，我建议培训6名博士学位学生（3个当前 + 3个新学生）和3名MSC学生（1个当前 + 2个新的）。