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

Muhammad Tahir, Ahmed Bouridane, Josef Kittler

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.
Original languageEnglish
Title of host publicationMultiple Classifier Systems
Place of PublicationLondon
PublisherSpringer
Pages283-294
Volume7872
ISBN (Print)978-3-642-38066-2
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Electronic)0302-9743

Fingerprint Dive into the research topics of 'Dimensionality reduction using stacked Kernel Discriminant Analysis for multi-label classification'. Together they form a unique fingerprint.

Cite this