Atypical Information Theory for Real-Valued Data

被引：0

作者：

Host-Madsen, Anders ^{[1
]}

Sabeti, Elyas ^{[1
]}

机构：

[1] Univ Hawaii, Dept Elect Engn, Honolulu, HI 96822 USA

来源：

2015 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT) | 2015年

关键词：

Atypicality; big data;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Atypical sequences are subsequences of long sequences that deviates from the 'normal' data. In a previous paper we have developed an information theory approach to such sequences for discrete data. In the current paper we extend this principle to real-valued data, whereby it is possible to use signal processing tools to search for atypical data. The application of this principle is to extract a few interesting sets of information from 'big data' sets. We include a simple application to stock market data.

引用

页码：666 / 670

页数：5

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