Sparsity and structures in large-scale machine learning problems

大规模机器学习问题中的稀疏性和结构

• 批准号: 2435101
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435101
• 关键词: Sparsity; structures; large; scale; machine

This project aims at investigating, both from a theoretical and computational point of view, the design of novel model-based and data-driven feature extraction and sparsification strategies for the approximation of complex Machine Learning problems. Special attention will be drawn to kernel-based and artificial-neural-network-based methods, which are two of the most important families of modern Machine Learning algorithms. The terminology "big data" is generally used to refer to datasets that are too large or complex for traditional data-processing technics to adequately deal with them. The exploration of such datasets with modern Machine Learning techniques therefore raises many theoretical and numerical challenges. The numerical complexity inherent to the processing of datasets indeed generally grows polynomially with their size, compromising de facto the analysis of very large datasets. In addition, the treatment of complex datasets often results in models involving a large number of parameters, making such models difficult to train while limiting their interpretability and increasing the risk of over/underfitting. Since such large-scale and complex datasets are more and more common in nowadays big-data and real-time-analytic era, their efficient processing is of great importance, not only at from purely scientific point of view, but also for many industrial and real-life applications.

该项目旨在从理论和计算的角度研究基于模型和数据驱动的新的特征提取和稀疏策略的设计，以逼近复杂的机器学习问题。将特别关注基于核的方法和基于人工神经网络的方法，这是现代机器学习算法的两个最重要的家族。术语“大数据”通常被用来指对于传统数据处理技术来说太大或太复杂而无法充分处理的数据集。因此，用现代机器学习技术探索这样的数据集提出了许多理论和数值挑战。处理数据集所固有的数值复杂性实际上通常随着数据集的大小而以多项式增长，这实际上损害了对非常大的数据集的分析。此外，对复杂数据集的处理往往导致模型涉及大量参数，使此类模型难以训练，同时限制了它们的可解释性，并增加了过度/不足拟合的风险。由于在当今大数据和实时分析时代，如此大规模和复杂的数据集越来越常见，因此它们的高效处理具有重要意义，不仅从纯科学的角度来看，而且对于许多工业和现实生活应用来说也是如此。