Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
2009 Special Issue: RKF-PCA: Robust kernel fuzzy PCA
Neural Networks
Robust kernel PCA using fuzzy membership
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Bagging classifiers for fighting poisoning attacks in adversarial classification tasks
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Maximum likelihood topology preserving ensembles
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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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.