An evaluation of dimension reduction techniques for one-class classification

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.