@inbook{76bb71323c0942dcad0432918697afb8,
title = "Dimensionality reduction using stacked Kernel Discriminant Analysis for multi-label classification",
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.",
keywords = "Dimensionality reduction, KDA using spectral regression, multi-label classification",
author = "Muhammad Tahir and Ahmed Bouridane and Josef Kittler",
year = "2013",
doi = "10.1007/978-3-642-38067-9_25",
language = "English",
isbn = "978-3-642-38066-2",
volume = "7872",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "283--294",
booktitle = "Multiple Classifier Systems",
address = "Germany",
}