Qualitative Performance Assessment of Adaptive Filtering and Machine Learning Algorithms

自适应过滤和机器学习算法的定性性能评估

• 批准号: EP/G032211/1
• 负责人: Danilo Mandic
• 金额: $ 19.48万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG032211%2F1
• 关键词: Qualitative; Performance; Assessment; Adaptive; Filtering

Signal modality characterisation, that is, the assessment of the linear, nonlinear, deterministics and stochastic signal content, is becoming an increasingly important area of multidisciplinary research. These ideas arose in Physics in the mid-1990s, however, the applications in machine learning and signal processing are only recently becoming apparent. As changes in the signal nature from, say, linear to nonlinear, can reveal e.g. health hazard, the signal processing framework should be chosen so as to preserve this critical information. However, standard learning algorithms are typically based on second order statistics, and will linearise naturally nonlinear phenomena.This proposal aims to provide a novel theoretical and computational framework for the design of learning algorithms with enhanced qualitative performance. Standard, second order statistics based adaptive filtering and machine learning algorithms are designed to optimise quantitative performance, and useful information is often lost. This type of problem arises typically in biomedical applications, for example, the change in the nature of brain electrical recordings from linear stochastic (ARMA) to nonlinear deterministic (chaotic) can indicate health hazard. The fundamental novelty of this work is a recently proposed, but not fully tested, delay vector variance (DVV) method which examines the local predictability and determinism of a signal in phase space, and provides a measure for the degree of linear, nonlinear, deterministic, and stochastic signal natures. This will serve as a framework to analyse the changes that signal processing and machine learning make to signal natures, and as a basis for the development of novel optimisation criteria which will both provide the required quantitative performance and preserve the fundamental signal nature to the desired degree. The team at Imperial have performed conceptual work related to this proposal, but no rigorous statistical evaluation or relavance analysis of the underlying state space features. The proposed research will perform comprehensive testing in order to provide enhanced understanding and insight into the qualitative peformance of learning algorithms used in biomedical applications. This will also lead to the design of novel adaptive learning algorithms capable of preserving the signal nature to a desired extent, a critical issue in several emerging applications.Solutions to these problems open new possibilities for advances in biomedical engineering, which underpins this research proposal, based at Imperial College and in collaboration with a leading applied biomedical group from Germany.

信号模态表征，即对线性、非线性、确定性和随机信号内容的评估，正成为多学科研究的一个日益重要的领域。这些想法在20世纪90年代中期出现在物理学中，然而，在机器学习和信号处理中的应用直到最近才变得明显。由于信号性质从线性到非线性的变化可以揭示例如健康危害，因此应选择信号处理框架以保留此关键信息。然而，标准的学习算法通常是基于二阶统计量，并会线性化自然nonlinearphenomen.This建议的目的是提供一个新的理论和计算框架的学习算法的设计与提高定性性能。标准的、基于二阶统计量的自适应滤波和机器学习算法被设计为优化定量性能，并且有用的信息经常丢失。这种类型的问题通常出现在生物医学应用中，例如，脑电记录的性质从线性随机（阿尔马）到非线性确定性（混沌）的变化可以指示健康危害。这项工作的基本新奇是最近提出的，但没有完全测试，延迟矢量方差（DVV）的方法，检查本地的可预测性和确定性的信号在相空间中，并提供了一个衡量的程度的线性，非线性，确定性和随机信号的性质。这将作为一个框架来分析信号处理和机器学习对信号性质的变化，并作为开发新的优化标准的基础，这些标准将提供所需的定量性能并将基本信号性质保持到所需的程度。帝国理工学院的团队已经完成了与该提议相关的概念性工作，但没有对潜在的状态空间特征进行严格的统计评估或相关性分析。拟议的研究将进行全面的测试，以提供更好的理解和洞察生物医学应用中使用的学习算法的定性性能。这也将导致新的自适应学习算法的设计，能够保持信号的性质，以所需的程度，在几个新兴的applications.Solutions的关键问题，这些问题的解决方案打开了新的可能性，在生物医学工程的进步，这支持了这项研究计划，在帝国理工学院的基础上，并与领先的应用生物医学组从德国合作。

项目成果

A Full Mean Square Analysis of CLMS for Second-Order Noncircular Inputs

二阶非循环输入的 CLMS 全均方分析

• DOI: 10.1109/tsp.2017.2739098
• 发表时间: 2017-11
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Xia Yili;M;ic Danilo P.
• 通讯作者: ic Danilo P.

Application of multivariate empirical mode decomposition for seizure detection in EEG signals

• DOI: 10.1109/iembs.2010.5626665
• 发表时间: 2010-11
• 期刊: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
• 作者: Naveed ur Rehman;Yili Xia;D. Mandic