Improving Performance and Accuracy of Local PCA
Local Principal Component Analysis (LPCA), also known as Clustered PCA (CPCA), is one of the popular techniques for dimensionality reduction and
data compression of large data sets encountered in computer graphics. The LPCA algorithm is a variant of k-means
clustering where the repetitive classification of high dimensional data points to their nearest cluster leads
to long execution times. The focus of this paper is on improving the efficiency and accuracy of LPCA. We propose
a novel SortCluster LPCA algorithm that significantly reduces the cost of the point-cluster classification stage,
achieving a speed-up of up to 20. To improve the approximation accuracy, we investigate different initialization
schemes for LPCA and find that the k-means++ algorithm [AV07] yields best results, however at a high computation
cost. We show that similar ideas that lead to the efficiency of our SortCluster LPCA algorithm can be
used to accelerate k-means++. The resulting initialization algorithm is faster than purely random seeding while
producing substantially more accurate data approximation.
Václav Gassenbauer, Jaroslav Kĝivánek, Kadi Bouatouch, Christian Bouville, and Mickaël Ribardiere
Improving Performance and Accuracy of Local PCA.
Computer Graphics Forum (Pacific Graphics), vol. 30, no. 7, 2011. ...
DOI | BibTeX.