Nonparametric mixtures of factor analyzers

The mixtures of factor analyzers (MFA) model allows data to be modeled as a mixture of Gaussians with a reduced parametrization. We present the formulation of a nonparametric form of the MFA model, the Dirichlet process MFA (DPMFA). The proposed model can be used for density estimation or clustering...

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Bibliographic Details
Published in2009 IEEE 17th Signal Processing and Communications Applications Conference pp. 708 - 711
Main Authors Gorur, D., Rasmussen, C.E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Computational complexity
Extracellular
Gaussian processes
Neurons
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Summary:The mixtures of factor analyzers (MFA) model allows data to be modeled as a mixture of Gaussians with a reduced parametrization. We present the formulation of a nonparametric form of the MFA model, the Dirichlet process MFA (DPMFA). The proposed model can be used for density estimation or clustering of high dimensional data. We utilize the DPMFA for clustering the action potentials of different neurons from extracellular recordings, a problem known as spike sorting. DPMFA model is compared to Dirichlet process mixtures of Gaussians model (DPGMM) which has a higher computational complexity. We show that DPMFA has similar modeling performance in lower dimensions when compared to DPGMM, and is able to work in higher dimensions.
ISBN:9781424444359
1424444357
ISSN:2165-0608
2693-3616
DOI:10.1109/SIU.2009.5136494