An evaluation of dimension reduction techniques for one-class classification |
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Authors: | Santiago D Villalba Pádraig Cunningham |
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Affiliation: | (1) Machine Learning Group, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland |
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Abstract: | Dimension reduction (DR) is important in the processing of data in domains such as multimedia or bioinformatics because such
data can be of very high dimension. Dimension reduction in a supervised learning context is a well posed problem in that there
is a clear objective of discovering a reduced representation of the data where the classes are well separated. By contrast
DR in an unsupervised context is ill posed in that the overall objective is less clear. Nevertheless successful unsupervised
DR techniques such as principal component analysis (PCA) exist—PCA has the pragmatic objective of transforming the data into
a reduced number of dimensions that still captures most of the variation in the data. While one-class classification falls
somewhere between the supervised and unsupervised learning categories, supervised DR techniques appear not to be applicable
at all for one-class classification because of the absence of a second class label in the training data. In this paper we
evaluate the use of a number of up-to-date unsupervised DR techniques for one-class classification and we show that techniques
based on cluster coherence and locality preservation are effective. |
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Keywords: | One class classification Dimensionality reduction Feature selection Feature transformation Principal component analysis Locality preservation Cluster coherence |
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