NDLI: A novel feature extraction algorithm

Content Provider	IEEE Xplore Digital Library
Author	Shi-Fei Ding Zhong-Zhi Shi Vun-Cheng Wang Shu-Shan Li
Copyright Year	2005
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Shandong Agric. Univ., Taian, China (Shi-Fei Ding)
Abstract	Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	1762
Ending Page	1767
File Size	403072
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Pattern recognition Data mining Algorithm design and analysis Principal component analysis Machine learning algorithms Pattern classification Machine learning Information theory Random variables pattern recognition probability information distance (PID) cross entropy PID criterion (PIDC) symmetry information feature extraction
Content Type	Text
Resource Type	Article

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