Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning
Barchiesi, D and Plumbley, MD (2015) Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning Journal of Signal Processing Systems, 79 (2). pp. 189-199.
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Abstract
In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment.
Item Type: | Article | |||||||||
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Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing | |||||||||
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Date : | 2015 | |||||||||
DOI : | 10.1007/s11265-014-0937-5 | |||||||||
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Additional Information : | The final publication is available at Springer via http://dx.doi.org/10.1007/s11265-014-0937-5 | |||||||||
Depositing User : | Symplectic Elements | |||||||||
Date Deposited : | 21 Apr 2015 14:25 | |||||||||
Last Modified : | 16 Aug 2015 01:08 | |||||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/807422 |
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