Multiple kernel learning for dimensionality reduction

Yen-Yu Lin*, Tyng Luh Liu, Chiou Shann Fuh

*Corresponding author for this work

Research output: Contribution to journalArticle

169 Scopus citations


In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: First, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.

Original languageEnglish
Article number5601738
Pages (from-to)1147-1160
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number6
StatePublished - 7 Apr 2011


  • Dimensionality reduction
  • face recognition
  • image clustering
  • multiple kernel learning
  • object categorization

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