Abstract
For learning-based tasks such as image classification, 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-EM (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. Extensive results show the effectiveness of our approach.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 903-917 |
| Number of pages | 15 |
| Journal | Pattern Recognition |
| Volume | 38 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2005 |
Funding
Dr. Huang is Member of the National Academy of Engineering; a Foreign Member of the Chinese Academies of Engineering and Sciences; and a Fellow of the International Association of Pattern Recognition and of the Optical Society of America. He has received a Guggenheim Fellowship, an A.V. Humboldt Foundation Senior US Scientist Award, and a Fellowship from the Japan Association for the Promotion of Science. He received the IEEE Signal Processing Society's Technical Achievement Award in 1987 and the Society Award in 1991. He was awarded the IEEE Third Millennium Medal in 2000. In addition, in 2000, he received the Honda Lifetime Achievement Award for “contributions to motion analysis.” In 2001, he received the IEEE Jack S. Kilby Medal. In 2002, he received the King-Sun Fu Prize from the International Association of Pattern Recognition and the Pan Wen-Yuan Outstanding Research Award. This work was supported in part by the Center for Infrastructure Assurance and Security (CIAS) Grant in University of Texas at San Antonio and by the National Science Foundation (NSF) under Grant IIS-03-08222 in Northwestern University. About the Author — YING WU received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois, in 2001. From 1997 to 2001, he was a research assistant at the Beckman Institute at UIUC. During summer 1999 and 2000, he was with Microsoft Research, Redmond, Washington. Since 2001, he has been an Assistant Professor at the Department of Electrical and Computer Engineering of Northwestern University, Evanston, Illinois. His current research interests include computer vision, machine learning, multimedia, human–computer interaction. He received the Robert T. Chien Award at UIUC, and is a recipient of the NSF CAREER award.
Keywords
- Discriminant analysis
- Image classification
- Kernel function
- Support vector machine
- Unlabeled data
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence