Ullrich, Torsten ; Fellner, Dieter W. (2011)
Linear Algorithms in Sublinear Time - a Tutorial on Statistical Estimation.
In: IEEE Computer Graphics and Applications, 31 (2)
doi: 10.1109/MCG.2010.21
Article, Bibliographie
Abstract
This tutorial presents probability theory techniques for boosting linear algorithms. The approach is based on statistics and uses educated guesses instead of comprehensive calculations. Because estimates can be calculated in sublinear time, many algorithms can benefit from statistical estimation. Several examples show how to significantly boost linear algorithms without negative effects on their results. These examples involve a Ransac algorithm, an image-processing algorithm, and a geometrical reconstruction. The approach exploits that, in many cases, the amount of information in a dataset increases asymptotically sublinearly if its size or sampling density increases. Conversely, an algorithm with expected sublinear running time can extract the most information.
| Item Type: | Article |
|---|---|
| Erschienen: | 2011 |
| Creators: | Ullrich, Torsten ; Fellner, Dieter W. |
| Type of entry: | Bibliographie |
| Title: | Linear Algorithms in Sublinear Time - a Tutorial on Statistical Estimation |
| Language: | English |
| Date: | 2011 |
| Journal or Publication Title: | IEEE Computer Graphics and Applications |
| Volume of the journal: | 31 |
| Issue Number: | 2 |
| DOI: | 10.1109/MCG.2010.21 |
| Abstract: | This tutorial presents probability theory techniques for boosting linear algorithms. The approach is based on statistics and uses educated guesses instead of comprehensive calculations. Because estimates can be calculated in sublinear time, many algorithms can benefit from statistical estimation. Several examples show how to significantly boost linear algorithms without negative effects on their results. These examples involve a Ransac algorithm, an image-processing algorithm, and a geometrical reconstruction. The approach exploits that, in many cases, the amount of information in a dataset increases asymptotically sublinearly if its size or sampling density increases. Conversely, an algorithm with expected sublinear running time can extract the most information. |
| Uncontrolled Keywords: | Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Business Field: Virtual engineering, Research Area: Confluence of graphics and vision, Research Area: Semantics in the modeling process, Computer graphics, Algorithms, Statistics, Optimization, Statistical computing, Algorithm boosting |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 04 Feb 2022 12:40 |
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