Neural networks and the bias/variance dilemma
Neural Computation
Bias/variance decompositions for likelihood-based estimators
Neural Computation
Recognition of Unconstrained Handwritten Numerals by Doubly Self-Organizing Neural Network
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Learning and example selection for object and pattern detection
Learning and example selection for object and pattern detection
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Pairwise vs global multi-class wrapper feature selection
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Detection of tuberculosis in sputum smear images using two one-class classifiers
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Outlier detection via localized p-value estimation
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
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Feature reduction is often an essential part of solving a classification task. One common approach for doing this, is Principal Component Analysis. There the low variance directions in the data are removed and the high variance directions are retained. It is hoped that these high variance directions contain information about the class differences. For one-class classification or novelty detection, the classification task contains one ill-determined class, for which (almost) no information is available. In this paper we show that for one-class classification, the low-variance directions are most informative, and that in the feature reduction a bias-variance trade-off has to be considered which causes that retaining the high variance directions is often not optimal.