Self-Supervised learning based on discriminative nonlinear features and its applications for pattern classification

Qi Tian, Ying Wu, Jie Yu, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

For learning-based tasks such as image classification and object recognition, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant expectation maximization (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification, hand posture recognition and fingertip tracking. Extensive results show the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationManaging Multimedia Semantics
PublisherIGI Global
Pages52-75
Number of pages24
ISBN (Print)9781591405696
DOIs
StatePublished - Dec 1 2005

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Self-Supervised learning based on discriminative nonlinear features and its applications for pattern classification'. Together they form a unique fingerprint.

  • Cite this