Cross-Entropy Clustering Approach to One-Class Classification

被引：4

作者：

Spurek, Przemysaw ^{[1
]}

Wojcik, Mateusz ^{[2
]}

Tabor, Jacek ^{[1
]}

机构：

[1] Jagiellonian Univ, Fac Math & Comp Sci, PL-30348 Krakow, Poland

[2] AGH Univ Sci & Technol, Fac Electrotech, Automat, Comp Sci & Biomed Engn, PL-30059 Krakow, Poland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I | 2015年 / 9119卷

关键词：

Covariance matrixa; Gaussian filter; Mathematical morphology; Electron microscopy; NOVELTY DETECTION; SUPPORT;

D O I：

10.1007/978-3-319-19324-3_43

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cross-entropy clustering (CEC) is a density model based clustering algorithm. In this paper we apply CEC to the one-class classification, which has several advantages over classical approaches based on Expectation Maximization (EM) and Support Vector Machines (SVM). More precisely, our model allows the use of various types of gaussian models with low computational complexity. We test the designed method on real data coming from the monitoring systems of wind turbines.

引用

页码：481 / 490

页数：10

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