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，以及通过网络的现成信息的现成可用性，从而给出了“大数据”时代。虽然传统的处理如此大量数据的方法是通过统计数据（例如平均值），但该项目的观点恰恰相反，即信息中的大多数值都在偏离平均水平的部分，这些部分是不寻常的，非典型的。熟悉的例子包括有价值的绘画或偏离非典型规范的著作。风险发展和科学研究也是如此。该项目的目的是从理论和实际的角度研究非典型性。拟议的工作在一般意义上使用了描述长度的信息理论概念来表征非典型性。该项目围绕两个推力。第一个是一种理论发展，可以准确地从信息理论的角度准确地确切地认为是非典型的。第二个是实施该理论的算法开发，尤其是要找到实施的快速算法，以便可以处理大数据集。大型数据集的分析，主要是生物学和医学中的大数据集，包括ECG（Electro Cardiogram）（Electro Cardiogram）和基因组学，将用于模拟，因为它们可以为人类心脏病的新诊断方法提供新的诊断方法。