Robust estimation for sparse data

Wen Hui Lo*, Sin-Horng Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always exists a coverage mismatch problem between the samples and its population in terms of probability density function (pdf) when the sample size is less than 20. We call this special problem the distribution mismatch (DM) problem. The paper proposes to solve the DM problem through addressing a new coverage-based estimator.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
DOIs
StatePublished - 1 Dec 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: 8 Dec 200811 Dec 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference2008 19th International Conference on Pattern Recognition, ICPR 2008
CountryUnited States
CityTampa, FL
Period8/12/0811/12/08

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    Lo, W. H., & Chen, S-H. (2008). Robust estimation for sparse data. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 [4761668] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2008.4761668