DocumentCode :
2524264
Title :
A property of learning chunk data using incremental kernel principal component analysis
Author :
Tokumoto, Takaomi ; Ozawa, Seiichi
Author_Institution :
Grad. Sch. of Eng., Kobe Univ., Kobe, Japan
fYear :
2012
fDate :
17-18 May 2012
Firstpage :
7
Lastpage :
10
Abstract :
An incremental learning algorithm of Kernel Principal Component Analysis (KPCA) called Chunk Incremental KPCA (CIKPCA) has been proposed for online feature extraction in pattern recognition. CIKPCA can reduce the number of times to solve the eigenvalue problem compared with the conventional incremental KPCA when a small number of data are simultaneously given as a stream of data chunks. However, our previous work suggests that the computational costs of the independent data selection in CIKPCA could dominate over those of the eigenvalue decomposition when a large chunk of data are given. To verify this, we investigate the influence of the chunk size to the learning time in CIKPCA. As a result, CIKPCA requires more learning time than IKPCA unless a large chunk of data are divided into small chunks (e.g., less than 50).
Keywords :
eigenvalues and eigenfunctions; learning (artificial intelligence); pattern recognition; principal component analysis; chunk incremental KPCA; eigenvalue problem; incremental kernel principal component analysis; incremental learning algorithm; independent data selection; learning chunk data; online feature extraction; pattern recognition; Earth; Learning systems; Principal component analysis; Remote sensing; Satellites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on
Conference_Location :
Madrid
Print_ISBN :
978-1-4673-1728-3
Electronic_ISBN :
978-1-4673-1726-9
Type :
conf
DOI :
10.1109/EAIS.2012.6232796
Filename :
6232796
Link To Document :
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