Dimension reduction for hidden Markov models using the suficiency approach

dc.creatorTomassi, Diego
dc.creatorForzani, Liliana
dc.creatorMilone, Diego Humberto
dc.creatorCook, R. Dennis
dc.date2011-08
dc.date2011
dc.date2021-09-21T14:17:43Z
dc.date.accessioned2023-07-15T03:26:20Z
dc.date.available2023-07-15T03:26:20Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/125252
dc.identifierissn:1850-2784
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7465713
dc.descriptionDimension reduction is often included in pattern recognizers based on hidden Markov models to lower the size of the models to estimate. Commonly used methods are heuristic in nature and do not take care of information retention after projection. In this paper, we present a new method based on the approach of suficient dimension reductions. It explicitly accounts for all the discriminative information available in the original features, while using a minimum number of linear combinations of them. We review the underlying theory and present an algorithm for the practical implementation of the proposed method. On the experimental side, we use simulations to illustrate its advantages over widely-used existing alternatives. In particular, we show that it performs as good as existing techniques when data is optimal according to the assumptions of those techniques, but signi cantly better for heteroscedastic data with no special structure on the covariance matrix.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format140-151
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Informáticas
dc.subjectMarkov models
dc.subjectDimension reduction
dc.titleDimension reduction for hidden Markov models using the suficiency approach
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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