Dimensionality reduction using stacked Kernel Discriminant Analysis for multi-label classification

Tahir, Muhammad, Bouridane, Ahmed and Kittler, Josef (2013) Dimensionality reduction using stacked Kernel Discriminant Analysis for multi-label classification. In: Multiple Classifier Systems. Lecture Notes in Computer Science, 7872 . Springer, London, pp. 283-294. ISBN 978-3-642-38066-2

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Abstract

Multi-label classification in which each instance may belong to more than one class is a challenging research problem. Recently, a considerable amount of research has been concerned with the development of "good" multi-label learning methods. Despite the extensive research effort, many scientific challenges posed by e.g. curse-of-dimensionality and correlation among labels remain to be addressed. In this paper, we propose a new approach to multi-label classification which combines stacked Kernel Discriminant Analysis using Spectral Regression (SR-KDA) with state-of-the-art instance-based multi-label (ML) learning method. The proposed system is validated on two multi-label databases. The results indicate significant performance gains when compared with the state-of-the art multi-label methods for multi-label classification.

Item Type:	Book Section
Uncontrolled Keywords:	Dimensionality reduction, KDA using spectral regression, multi-label classification
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Library Catalogue
Depositing User:	Becky Skoyles
Date Deposited:	19 Jan 2015 10:46
Last Modified:	12 Oct 2019 21:50
URI:	http://nrl.northumbria.ac.uk/id/eprint/20948

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