A Unified Framework for Multiscale Machine Learning at the Edge

边缘多尺度机器学习的统一框架

• 批准号: EP/V046837/1
• 负责人: Matthew Nunes
• 金额: $ 24.67万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV046837%2F1
• 关键词: Unified; Framework; Multiscale; Machine; Learning

Advances in data storage capabilities and an increasingly technology-driven society has resulted in the collection of vast quantities of high quality, varied and high-dimensional data. This 'big-data revolution' has in turn spurred a recent explosion in research in machine learning (ML), both applied and theoretical -- it is difficult to imagine a business sector or scientific field in which machine learning hasn't pushed forward high-throughput data analysis. Due to increasing complexity of algorithms, machine learning is often performed in `the cloud`. A drawback of this is that data needs to be transferred to a central location and processed before individual devices are updated, which consumes a lot of energy and time. In addition, users are increasingly aware of potential security issues surrounding moving data between virtual locations. There is thus a need for machine learning tasks being performed `at the edge', for example on activity trackers, mobile phones or other smart devices. These situations typically have access to a comparatively small amount of memory and data processing capability. Researchers thus need to develop new, application-tailored machine learning algorithms for these resource-constrained environments. In these settings, algorithm efficiency and the construction of low-dimensional features for learning is of the utmost importance.A separate issue often faced when using many machine learning algorithms is that they often have difficulties in representing data with complicated features or structure, such as shapes and directional information. This structure is often encountered in e.g. acoustic data or biomedical images. In addition, some methods do not handle missing data well; this can hamper accurate decision-making with machine learning. For several years the PI has been at the forefront of developments in using wavelet and other multiscale signal processing methods, creating new techniques which relax traditional assumptions, and using them in new and innovative ways. Our proposal aims to address the drawbacks outlined above by developing new machine learning algorithms using so-called wavelet lifting techniques. Since such methods operate on data at different "scales", they are well-placed to represent time-varying structure or directional spatial shapes at different resolutions and across dimensions. They can also naturally handle missing data by adapting to available data sampling structures, and are memory-efficient since they use data replacement operations. Our approach will integrate these algorthms with machine learning learning methodology to widen the ability of learning algorithms to be used on low-memory devices. We aim to achieve improved robustness to missing data and test our developed methodology in a wide range of machine learning tasks, for example facial recognition, acoustic signal processing and pattern detection.

数据存储能力的进步和越来越多的技术驱动的社会导致了大量高质量，多样化和高维数据的收集。这场“大数据革命”反过来又刺激了最近机器学习（ML）研究的爆炸式发展，无论是应用还是理论--很难想象机器学习没有推动高吞吐量的商业部门或科学领域。数据分析。由于算法的复杂性越来越高，机器学习通常在“云”中进行。这样做的一个缺点是，数据需要传输到一个中央位置，并在更新单个设备之前进行处理，这会消耗大量的能量和时间。此外，用户越来越意识到在虚拟位置之间移动数据的潜在安全问题。因此，需要“在边缘”执行机器学习任务，例如在活动跟踪器、移动的电话或其他智能设备上。这些情况通常只能访问相对少量的存储器和数据处理能力。因此，研究人员需要为这些资源受限的环境开发新的、应用定制的机器学习算法。在这些环境中，算法效率和用于学习的低维特征的构建至关重要。使用许多机器学习算法时经常面临的另一个问题是，它们通常难以表示具有复杂特征或结构的数据，例如形状和方向信息。这种结构经常在例如声学数据或生物医学图像中遇到。此外，有些方法不能很好地处理缺失数据;这可能会妨碍机器学习的准确决策。几年来，PI一直处于使用小波和其他多尺度信号处理方法的发展前沿，创造了放松传统假设的新技术，并以新的创新方式使用它们。我们的建议旨在通过使用所谓的小波提升技术开发新的机器学习算法来解决上述缺点。由于这些方法在不同的“尺度”上对数据进行操作，因此它们很好地用于表示不同分辨率和跨维度的时变结构或方向空间形状。它们还可以通过适应可用的数据采样结构来自然地处理丢失的数据，并且由于它们使用数据替换操作而具有内存效率。我们的方法将这些算法与机器学习学习方法相结合，以扩大学习算法在低内存设备上使用的能力。我们的目标是提高对缺失数据的鲁棒性，并在广泛的机器学习任务中测试我们开发的方法，例如面部识别，声学信号处理和模式检测。

项目成果

Trend locally stationary wavelet processes

• DOI: 10.1111/jtsa.12643
• 发表时间: 2022-11
• 期刊: Journal of Time Series Analysis
• 影响因子: 0.9
• 作者: Euan T. McGonigle;Rebecca Killick;M. Nunes

Rejoinder to the discussions of "Spatial+: A novel approach to spatial confounding".

反驳“空间：一种解决空间混杂的新颖方法”的讨论。

• DOI: 10.1111/biom.13653
• 发表时间: 2022
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Dupont E

Spatial Confounding and Spatial+ for Nonlinear Covariate Effects

非线性协变量效应的空间混杂和空间

• DOI: 10.1007/s13253-023-00586-7
• 发表时间: 2023
• 期刊: Journal of Agricultural, Biological and Environmental Statistics
• 作者: Dupont E