Covariance-based PCA for multi-size data

Menghua Zhai, Feiyu Shi, Drew Duncan, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Principal component analysis (PCA) is used in diverse settings for dimensionality reduction. If data elements are all the same size, there are many approaches to estimating the PCA decomposition of the dataset. However, many datasets contain elements of different sizes that must be coerced into a fixed size before analysis. Such approaches introduce errors into the resulting PCA decomposition. We introduce CO-MPCA, a nonlinear method of directly estimating the PCA decomposition from datasets with elements of different sizes. We compare our method with two baseline approaches on three datasets: a synthetic vector dataset, a synthetic image dataset, and a real dataset of color histograms extracted from surveillance video. We provide quantitative and qualitative evidence that using CO-MPCA gives a more accurate estimate of the PCA basis.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Number of pages6
ISBN (Electronic)9781479952083
StatePublished - Dec 4 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: Aug 24 2014Aug 28 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference22nd International Conference on Pattern Recognition, ICPR 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Covariance-based PCA for multi-size data'. Together they form a unique fingerprint.

Cite this