A Unified Framework for Multiscale Machine Learning at the Edge

数据存储能力的进步和日益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。这种“大数据革命”反过来又刺激了最近机器学习（ML）研究的爆炸式增长，无论是应用还是理论——很难想象机器学习没有推动高通量数据分析的商业部门或科学领域。由于算法越来越复杂，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心位置，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，用户越来越意识到围绕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、手机或其他智能设备上。这些情况通常只能访问相对较少的内存和数据处理能力。因此，研究人员需要为这些资源受限的环境开发新的、适合应用的机器学习算法。在这些情况下，算法效率和低维特征的构建是至关重要的。在使用许多机器学习算法时经常面临的一个单独问题是，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，并以新的和创新的方式使用它们。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。由于这些方法对不同“尺度”的数据进行操作，因此它们可以很好地表示不同分辨率和跨维度的时变结构或定向空间形状。它们还可以通过适应可用的数据采样结构自然地处理丢失的数据，并且由于它们使用数据替换操作，因此具有内存效率。我们的方法将这些算法与机器学习方法相结合，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。

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.