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Bounds on the quality of the PCA bounding boxes

Published: 01 October 2009 Publication History

Abstract

Principal component analysis (PCA) is commonly used to compute a bounding box of a point set in R^d. The popularity of this heuristic lies in its speed, easy implementation and in the fact that usually, PCA bounding boxes quite well approximate the minimum-volume bounding boxes. We present examples of discrete points sets in the plane, showing that the worst case ratio of the volume of the PCA bounding box and the volume of the minimum-volume bounding box tends to infinity. Thus, we concentrate our attention on PCA bounding boxes for continuous sets, especially for the convex hull of a point set. Here, we contribute lower bounds on the approximation factor of PCA bounding boxes of convex sets in arbitrary dimension, and upper bounds in R^2 and R^3.

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  • (2020)Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM AutoencodersArtificial Neural Networks and Machine Learning – ICANN 202010.1007/978-3-030-61609-0_23(284-296)Online publication date: 15-Sep-2020
  • (2018)Gradient-based multi-component topology optimization for stamped sheet metal assemblies (MTO-S)Structural and Multidisciplinary Optimization10.1007/s00158-017-1878-y58:1(83-94)Online publication date: 1-Jul-2018
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Information & Contributors

Information

Published In

cover image Computational Geometry: Theory and Applications
Computational Geometry: Theory and Applications  Volume 42, Issue 8
October, 2009
92 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2009

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  1. Bounding boxes
  2. Principal component analysis

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View all
  • (2024)Post-processing Workflow for 3D Vietnamese Historical Printing Woodblocks Restoration from 2D printsProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654574(203-208)Online publication date: 23-Feb-2024
  • (2020)Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM AutoencodersArtificial Neural Networks and Machine Learning – ICANN 202010.1007/978-3-030-61609-0_23(284-296)Online publication date: 15-Sep-2020
  • (2018)Gradient-based multi-component topology optimization for stamped sheet metal assemblies (MTO-S)Structural and Multidisciplinary Optimization10.1007/s00158-017-1878-y58:1(83-94)Online publication date: 1-Jul-2018
  • (2011)Fast oriented bounding box optimization on the rotation group SO(3,ℝ)ACM Transactions on Graphics10.1145/2019627.201964130:5(1-16)Online publication date: 22-Oct-2011

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