Supervised classification for functional data: A weighted distance approach
Computational Statistics & Data Analysis, ISSN: 0167-9473, Vol: 56, Issue: 7, Page: 2334-2346
2012
- 26Citations
- 33Captures
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Article Description
A natural methodology for discriminating functional data is based on the distances from the observation or its derivatives to group representative functions (usually the mean) or their derivatives. It is proposed to use a combination of these distances for supervised classification. Simulation studies show that this procedure performs very well, resulting in smaller testing classification errors. Applications to real data show that this technique behaves as well as–and in some cases better than–existing supervised classification methods for functions.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167947312000333; http://dx.doi.org/10.1016/j.csda.2012.01.013; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84857628100&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0167947312000333; https://api.elsevier.com/content/article/PII:S0167947312000333?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0167947312000333?httpAccept=text/plain; https://dx.doi.org/10.1016/j.csda.2012.01.013
Elsevier BV
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