DocumentCode :
2816937
Title :
Batch-incremental principal component analysis with exact mean update
Author :
Duan, Guifang ; Chen, Yen-wei
Author_Institution :
Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
fYear :
2011
fDate :
11-14 Sept. 2011
Firstpage :
1397
Lastpage :
1400
Abstract :
Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting function is proposed for contribution balance of the current data and the new observed data to the new subspace. The performance of our method is illustrated in the experiments on face modeling and face recognition.
Keywords :
computer vision; face recognition; learning (artificial intelligence); principal component analysis; batch-incremental principal component analysis; computer vision; exact mean update; face modeling; face recognition; incremental learning procedure; machine learning; subspace updating; Conferences; Covariance matrix; Face; Image reconstruction; Principal component analysis; Training; Vectors; batch-incremental learning; exact mean update; principal component analysis (PCA); weighting matrix;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
ISSN :
1522-4880
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2011.6115700
Filename :
6115700
Link To Document :
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