Paper Info
Title
On-line versus off-line accelerated kernel feature analysis: Application to computer-aided detection of polyps in CT colonography
Abstract
A semi-supervised learning method, the on-line accelerated kernel feature analysis (On-line AKFA) is presented. In On-line AKFA, features are extracted while data are being fed to the algorithm in small batches as the algorithm proceeds. The paper compares and contrasts the use of On-line AKFA and Off-line AKFA in CT colonography. On-line AKFA provides the flexibility to allow the feature space to dynamically adjust to changes in the input data with time during the training phase. The computational time, reconstruction accuracy, projection variance, and classification performance of the proposed method are experimentally evaluated for kernel principal component analysis (KPCA), Off-line AKFA, and On-line AKFA. Experimental results demonstrate a significant reduction in computation time for On-line AKFA compared to the other feature extraction methods considered in this paper.
Year
DOI
Venue
2010
10.1016/j.sigpro.2009.07.004
Signal Processing
Keywords
Field
DocType
input data,ct colonography,kernel principal component analysis,computational time,on-line akfa,feature extraction method,computer-aided detection,feature space,off-line accelerated kernel feature,off-line akfa,computation time,on-line accelerated kernel feature,algorithm proceed,semi supervised learning,feature extraction,feature analysis
Kernel (linear algebra),Feature vector,Off line,Pattern recognition,Computer science,Kernel principal component analysis,Feature extraction,Contrast (statistics),Artificial intelligence,Pattern recognition (psychology),Computation
Journal
Volume
Issue
ISSN
90
8
Signal Processing
Citations 
PageRank 
References 
6
0.51
20
Authors
3
Name
Order
Citations
PageRank
Lahiruka Winter160.51
Yuichi Motai223024.68
Alen Docef3192.78