Privileged information for data clustering

Feyereisl, Jan; Aickelin, Uwe

Abstract

Many machine learning algorithms assume that all input samples are independently and identically distributed from
some common distribution on either the input space X, in the case of unsupervised learning, or the input and output
space X Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this
assumption has been explored and the importance of incorporation of additional information within machine learning
algorithms became more apparent. Traditionally such fusion of information was the domain of semi-supervised
learning. More recently the inclusion of knowledge from separate hypothetical spaces has been proposed by Vapnik
as part of the supervised setting. In this work we are interested in exploring Vapnik’s idea of ‘master-class’ learning
and the associated learning using ‘privileged’ information, however within the unsupervised setting.
Adoption of the advanced supervised learning paradigm for the unsupervised setting instigates investigation into
the dierence between privileged and technical data. By means of our proposed aRi-MAX method stability of the
K-Means algorithm is improved and identification of the best clustering solution is achieved on an artificial dataset.
Subsequently an information theoretic dot product based algorithm called P-Dot is proposed. This method has the
ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and
technical data for improved clustering. Application of the P-Dot method to the task of digit recognition confirms our
findings in a real-world scenario.

Citation

Feyereisl, J., & Aickelin, U. (2012). Privileged information for data clustering. Information Sciences, 194, https://doi.org/10.1016/j.ins.2011.04.025

• Journal Article Type: Article
• Publication Date: Jan 1, 2012
• Deposit Date: Jun 17, 2013
• Publicly Available Date: Jun 17, 2013
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Electronic ISSN: 1872-6291
• Publisher: Elsevier
• Peer Reviewed: Not Peer Reviewed
• Volume: 194
• DOI: https://doi.org/10.1016/j.ins.2011.04.025
• Public URL: https://nottingham-repository.worktribe.com/output/1008689
• Publisher URL: http://dx.doi.org/10.1016/j.ins.2011.04.025
• Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 194, (2012), doi: 10.1016/j.ins.2011.04.025