CIF:EAGER:Information Theory Approaches for finding Atypical Sequences

CIF:EAGER:寻找非典型序列的信息论方法

• 批准号: 1434600
• 负责人: Anders Host-Madsen
• 金额: $ 7.96万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1434600&HistoricalAwards=false
• 关键词: CIF; EAGER; Information; Theory; Approaches

One characteristic of the information age is the exponential growth of information, and the ready availability of this information through networks, giving the "Big Data" era. Whereas the conventional approach to treating such large volumes of data is through statistics, such as averages, the perspective in this project is the opposite, namely that most of the value in the information is in the parts that deviate from the average, that are unusual, atypical. Familiar examples include valuable paintings or writings that deviate from the norms, that are atypical. The same could be true for venture development and scientific research. The goal of this project is to investigate atypicality both from a theoretical and a practical point of view. The proposed work uses the information theory concept of descriptive length, in a general sense, to characterize atypicality. The project is oriented around two thrusts. The first is a theoretical development to make precise exactly what should be considered atypical from an information theory point of view. The second is algorithm development to implement this theory, and in particular to find fast algorithms for implementation so that big data sets can be processed.Analysis of large data sets, mainly in biology and medicine, including ECG (electro cardiogram) and genomics, will be used in simulations, as they can lead to new diagnostic methods for human cardiac disease.

信息时代的一个特征是信息的指数增长，以及通过网络随时可用的信息，这就产生了“大数据”时代。虽然处理如此大量数据的传统方法是通过统计数据，如平均值，但本项目的观点恰恰相反，即信息中的大部分价值都在偏离平均值的部分，这些部分是不寻常的，非典型的。常见的例子包括偏离规范的有价值的绘画或作品，这是非典型的。风险开发和科学研究也是如此。这个项目的目标是从理论和实践的角度来研究重复性。建议的工作使用信息理论的描述性长度的概念，在一般意义上说，来表征重复性。该项目围绕两个重点。首先是理论上的发展，以准确地从信息论的角度来看，什么应该被认为是非典型的。第二个目标是实现这一理论的算法开发，特别是寻找快速算法，以便处理大数据集。对大数据集的分析，主要是在生物学和医学领域，包括ECG（心电图）和基因组学，将用于模拟，因为它们可以为人类心脏疾病带来新的诊断方法。