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.
|Title of host publication||Proceedings - International Conference on Pattern Recognition|
|Number of pages||6|
|State||Published - Dec 4 2014|
|Event||22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden|
Duration: Aug 24 2014 → Aug 28 2014
|Name||Proceedings - International Conference on Pattern Recognition|
|Conference||22nd International Conference on Pattern Recognition, ICPR 2014|
|Period||8/24/14 → 8/28/14|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition