ncku-talk2009-05-07

NCKU Math colloquium

DATE 2009-05-07　16:10-17:00

PLACE 國家理論科學中心 204 室

SPEAKER 李育杰副教授（國立台灣科技大學資訊工程系）

TITLE Anomaly Detection via Over-sampling Principal Component Analysis

ABSTRACT Outlier detection is an important issue in data mining and has been studied in different research areas. It can be used for detecting the small amount of deviated data. In this article, we use "Leave One Out" procedure to check each individual point the "with or without" effect on the variation of principal directions.
Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier). Except for identifying the suspicious outliers, we also design an on-line anomaly detection to detect the new arriving anomaly. In addition, we also study the quick updating of the principal directions for the effective computation and satisfying the on-line detecting demand. Numerical experiments show that our proposed method is effective in computation time and anomaly detection.

NCKU Math colloquium

DATE	2009-05-07　16:10-17:00

PLACE	國家理論科學中心 204 室

SPEAKER	李育杰副教授（國立台灣科技大學資訊工程系）

TITLE	Anomaly Detection via Over-sampling Principal Component Analysis

ABSTRACT	Outlier detection is an important issue in data mining and has been studied in different research areas. It can be used for detecting the small amount of deviated data. In this article, we use "Leave One Out" procedure to check each individual point the "with or without" effect on the variation of principal directions. Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier). Except for identifying the suspicious outliers, we also design an on-line anomaly detection to detect the new arriving anomaly. In addition, we also study the quick updating of the principal directions for the effective computation and satisfying the on-line detecting demand. Numerical experiments show that our proposed method is effective in computation time and anomaly detection.