caa a22 4500 463207084 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20070401xx s 000 0 eng 10.1007/s10462-008-9082-5 doi (NATIONALLICENCE)springer-10.1007/s10462-008-9082-5 An evaluation of dimension reduction techniques for one-class classification [Elektronische Daten] [Santiago Villalba, Pádraig Cunningham] 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. Springer Science+Business Media B.V., 2008 One class classification nationallicence Dimensionality reduction nationallicence Feature selection nationallicence Feature transformation nationallicence Principal component analysis nationallicence Locality preservation nationallicence Cluster coherence nationallicence Villalba Santiago Machine Learning Group, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut Cunningham Pádraig Machine Learning Group, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut Artificial Intelligence Review Springer Netherlands 27/4(2007-04-01), 273-294 0269-2821 27:4<273 2007 27 10462 https://doi.org/10.1007/s10462-008-9082-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-008-9082-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Villalba Santiago Machine Learning Group, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut NATIONALLICENCE 700 1- Cunningham Pádraig Machine Learning Group, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 27/4(2007-04-01), 273-294 0269-2821 27:4<273 2007 27 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer