Multi-label classification using stacked spectral kernel discriminant analysis

Tahir, Muhammad, Kittler, Josef and Bouridane, Ahmed (2016) Multi-label classification using stacked spectral kernel discriminant analysis. Neurocomputing, 171. pp. 127-137. ISSN 0925-2312

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Abstract

Multi-label classification is a challenging research problem due to the fact that each example may belong to a varying number of classes. This problem can be further aggravated by high dimensionality and complex correlation among labels. In this paper, a discriminant approach to multi-label classification is proposed using the concept of stacking and spectral regression based kernel discriminant analysis (SSRKDA). For effective stacked generalisation, a novel fast implementation of the leave-one-out cross-validation for SSRKDA is also presented in this paper. The proposed system is validated on several multi-label databases. The results indicate a significant boost in performance when SSRKDA is compared to other multi-label classification techniques.

Item Type:	Article
Uncontrolled Keywords:	Multilabel classification; Stacked Kernel Discriminant Analysis; Leave-one-out Cross Validation; Multi-label Nearest Neighbour Classifier
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Ay Okpokam
Date Deposited:	28 Jul 2015 09:02
Last Modified:	17 Nov 2020 12:07
URI:	http://nrl.northumbria.ac.uk/id/eprint/23452

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