| Paper: | MLSP-L1.3 | ||
| Session: | Pattern Recognition and Classification I | ||
| Time: | Thursday, May 20, 10:10 - 10:30 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | LEARNING BASED ON KERNEL DISCRIMINANT-EM ALGORITHM FOR IMAGE CLASSIFICATION | ||
| Authors: | Qi Tian; University of Texas, San Antonio | ||
| Jerry (Jie) Yu; University of Texas, San Antonio | |||
| Ying Wu; Northwestern University | |||
| Thomas S. Huang; University of Illinois at Urbana-Champaign | |||
| Abstract: | In image classification and other learning-based object recognition tasks, it is often tedious and expensive to label large training data sets. Discriminant-EM (DEM) proposed a semi-supervised learning framework which takes both labeled and unlabeled data to learn classifiers. This paper extends the linear D-EM to nonlinear kernel algorithm, KDEM and evaluates KDEM systematically on both benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated. | ||
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