Please use this identifier to cite or link to this item: https://doi.org/10.1007/11573937_51
Title: Principal component analysis for distributed data sets with updating
Authors: Bai, Z.-J. 
Chan, R.H.
Luk, F.T.
Issue Date: 2005
Source: Bai, Z.-J., Chan, R.H., Luk, F.T. (2005). Principal component analysis for distributed data sets with updating. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3756 LNCS : 471-483. ScholarBank@NUS Repository. https://doi.org/10.1007/11573937_51
Abstract: Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clustering algorithms are effective when the data sets are found in the same location. In applications where the large data sets are physically far apart, moving huge amounts of data to a single location can become an impractical, or even impossible, task. A way around this problem was proposed in [10], where truncated singular value decompositions (SVDs) are computed locally and used to reduce the communication costs. Unfortunately, truncated SVDs introduce local approximation errors that could add up and would adversely affect the accuracy of the final PCA. In this paper, we introduce a new method to compute the PCA without incurring local approximation errors. In addition, we consider the situation of updating the PCA when new data arrive at the various locations. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/131628
ISBN: 3540296395
ISSN: 03029743
DOI: 10.1007/11573937_51
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

19
checked on Jan 17, 2018

Page view(s)

10
checked on Jan 14, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.