MPCA: EM-based PCA for mixed-size image datasets

Feiyu Shi, Menghua Zhai, Drew Duncan, Nathan Jacobs

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

2 Scopus citations


Principal component analysis (PCA) is a widely used technique for dimensionality reduction which assumes that the input data can be represented as a collection of fixed-length vectors. Many real-world datasets, such as those constructed from Internet photo collections, do not satisfy this assumption. A natural approach to addressing this problem is to first coerce all input data to a fixed size, and then use standard PCA techniques. This approach is problematic because it either introduces artifacts when we must upsample an image, or loses information when we must downsample an image. We propose MPCA, an approach for estimating the PCA decomposition from multi-sized input data which avoids this initial resizing step. We demonstrate the effectiveness of this approach on simulated and real-world datasets.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Number of pages5
ISBN (Electronic)9781479957514
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • dimensionality reduction
  • expectation-maximization
  • nonlinear optimization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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