Portal de Periódicos da CAPES

Outlier Resistant PCA Ensembles

2006; Springer Science+Business Media; Linguagem: Inglês

10.1007/11893011_55

1611-3349

Bogdan Gabryś, Bruno Baruque, Emilio Corchado,

Anomaly Detection Techniques and Applications

Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generation of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of Principal Component Analysis (PCA). We show that the proposed PCA ensembles exhibit a much more robust behaviour in the presence of outliers which can seriously affect the performance of an individual PCA algorithm. The performance and characteristics of the proposed approaches are illustrated on a number of experimental studies where an individual PCA is compared to the introduced PCA ensemble.