Multi-label classification using stacked spectral kernel discriminant analysis

Muhammad Tahir, Josef Kittler, Ahmed Bouridane

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)127-137
Number of pages11
JournalNeurocomputing
Volume171
Early online date25 Jun 2015
DOIs
Publication statusPublished - 1 Jan 2016

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