Semi-supervised Spectral Connectivity Projection Pursuit

Hofmeyr, David and Pavlidis, Nicos Georgios (2015) Semi-supervised Spectral Connectivity Projection Pursuit. In: Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2015. IEEE, pp. 201-206. ISBN 9781467374507

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Abstract

We propose a projection pursuit method based on semi-supervised spectral connectivity. The projection index is given by the second eigenvalue of the graph Laplacian of the projected data. An incomplete label set is used to modify pairwise similarities between data in such a way that penalises projections which do not admit a separation of the classes (within the training data). We show that the global optimum of the proposed problem converges to the Transductive Support Vector Machine solution, as the scaling parameter is reduced to zero. We evaluate the performance of the proposed method on benchmark data sets.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
81182
Deposited By:
Deposited On:
22 Aug 2016 08:34
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 10:42