Görür, D and Rasmussen, CE (2009) Nonparametric mixtures of factor analyzers. 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009. pp. 708-711.Full text not available from this repository.
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. ©2009 IEEE.
|Divisions:||Div F > Computational and Biological Learning|
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|Date Deposited:||16 Jul 2015 14:12|
|Last Modified:||03 Sep 2015 05:07|