A trace ratio maximization approach to multiple kernel-based dimensionality reduction

Wenhao Jiang, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings.
Original languageEnglish
Pages (from-to)96-106
Number of pages11
JournalNeural Networks
Volume49
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Dimensionality reduction
  • Graph embedding
  • Kernel learning
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this