Multi-Modal Signal Processing On Non-Euclidean Domains

非欧几里得域上的多模态信号处理

• 批准号: 2283930
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2283930
• 关键词: Multi; Modal; Signal; Processing; Non

As the world enters the era of big data, multi-dimensional data are being generated on an unprecedented level, which are so large and complex that cannot be processed using a standard computer. In addition to the sheer volume of information, new complex sets of data, such as social networks and protein function networks, are stored in irregular data structures such as graphs, which cannot be processed using classical methods. Modern data problems require solutions that can extract insights from an overwhelming sea of information, which should be done in a robust, efficient, and reliable manner. However, this poses significant challenges for classical signal processing and machine learning techniques, which have been designed to work with small-scale data problems defined on regular structures such as image and speech.To address the challenges of big data, this project will explore and develop efficient data analytics methods to extract information from high-dimensional data defined on irregular structures. This is in line with the EPSRC research area of Artificial Intelligence and Digital Signal Processing, under the umbrella of the strategic theme of Information and Communication Technology. Specifically, the project will use tools from the emerging fields of Tensor Decomposition (TD) and Graph Signal Processing (GSP). Tensors are multi-dimensional generalization of vectors and matrices, while TD aims to represent large tensors efficiently using smaller core tensors, hence bypassing many computational problems inherent to the nature of big data. GSP on the other hand aims to extract insights from signals defined on top of graphs, which is done by considering the underlying structure of the graph. TD and GSP are two fields that have recently gained a lot of tractions, but not much has been done in terms of merging the two disciplines. For instance, most TD applications ignore the irregular data domain, while most GSP applications don't consider multi-dimensional tensors defined on top of graphs. Given the relevance of both fields, it is important to develop unified techniques that can efficiently process high dimensional data by leveraging the underlying data structure. This offers promising applications in many areas, such as bioinformatics and finance, where the interaction between high-dimensional data heavily depends on the underlaying network.

随着世界进入大数据时代，多维度的数据正在以前所未有的水平产生，这些数据非常庞大和复杂，无法用标准的计算机处理。除了大量的信息，新的复杂数据集，如社会网络和蛋白质函数网络，存储在不规则的数据结构中，如图，这是不能用经典方法处理的。现代数据问题需要能够从海量信息中提取见解的解决方案，这应该以稳健、高效和可靠的方式完成。然而，这对经典的信号处理和机器学习技术提出了重大挑战，这些技术被设计用于处理在规则结构（如图像和语音）上定义的小规模数据问题。为了应对大数据的挑战，该项目将探索和开发有效的数据分析方法，从不规则结构上定义的高维数据中提取信息。这与EPSRC在信息与通信技术战略主题下的人工智能和数字信号处理研究领域相一致。具体来说，该项目将使用新兴领域张量分解（TD）和图信号处理（GSP）的工具。张量是向量和矩阵的多维泛化，而TD旨在使用较小的核心张量有效地表示大张量，从而绕过了大数据本质固有的许多计算问题。另一方面，GSP旨在从定义在图顶部的信号中提取洞察力，这是通过考虑图的底层结构来完成的。TD和GSP是最近获得了很多关注的两个领域，但在合并这两个学科方面做得并不多。例如，大多数TD应用程序忽略不规则数据域，而大多数GSP应用程序不考虑在图上定义的多维张量。考虑到这两个领域的相关性，开发能够利用底层数据结构有效处理高维数据的统一技术是很重要的。这在许多领域提供了有前途的应用，如生物信息学和金融，其中高维数据之间的交互严重依赖于底层网络。