Technical Program

Paper Detail

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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