Improving Performance and Accuracy of Local PCA

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dc.contributor.authorGassenbauer, Václaven_US
dc.contributor.authorKrivánek, Jaroslaven_US
dc.contributor.authorBouatouch, Kadien_US
dc.contributor.authorBouville, Christianen_US
dc.contributor.authorRibardière, Mickaëlen_US
dc.contributor.editorBing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. Linen_US
dc.date.accessioned2015-02-27T16:12:59Z
dc.date.available2015-02-27T16:12:59Z
dc.date.issued2011en_US
dc.description.abstractLocal Principal Component Analysis (LPCA) 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 kmeans 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.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/j.1467-8659.2011.02047.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.02047.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleImproving Performance and Accuracy of Local PCAen_US
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