PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric mixtures of factor analyzers
Dilan Gorur and Carl Edward Rasmussen
In: SIU 2009, 9-11 April 2009, Turkey.

Abstract

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 dimensiona 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.

EPrint Type:Conference or Workshop Item (Oral)
Additional Information:in Turkish
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:5349
Deposited By:Dilan Gorur
Deposited On:24 March 2009