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Learning incoherent subspaces for classification via supervised iterative projections and rotations

Barchiesi, D and Plumbley, MD (2013) Learning incoherent subspaces for classification via supervised iterative projections and rotations In: 2013 IEEE International Workshop on Machine Learning for Signal Processing, 2013-09-22 - 2013-09-22, Southampton, United Kingdom.

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Abstract

In this paper we present the supervised iterative projections and rotations (S-IPR) algorithm, a method to optimise a set of discriminative subspaces for supervised classification. We show how the proposed technique is based on our previous unsupervised iterative projections and rotations (IPR) algorithm for incoherent dictionary learning, and how projecting the features onto the learned sub-spaces can be employed as a feature transform algorithm in the context of classification. Numerical experiments on the FISHERIRIS and on the USPS datasets, and a comparison with the PCA and LDA methods for feature transform demonstrates the value of the proposed technique and its potential as a tool for machine learning. © 2013 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
Barchiesi, DUNSPECIFIEDUNSPECIFIED
Plumbley, MDUNSPECIFIEDUNSPECIFIED
Date : 1 December 2013
Funders : EPSRC
Identification Number : 10.1109/MLSP.2013.6661981
Grant Title : the Platform Grant, the Leadership Fellowship
Copyright Disclaimer : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published: In Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Sept. 22-25, 2013, Southampton, UK. doi:10.1109/MLSP.2013.6661981
Depositing User : Symplectic Elements
Date Deposited : 16 Aug 2016 07:53
Last Modified : 16 Aug 2016 07:53
URI: http://epubs.surrey.ac.uk/id/eprint/811708

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