/mmdpnc

maximum margin dirichlet process mixtures for clustering

Primary LanguageMatlab

mmdpnc

maximum margin dirichlet process mixtures for clustering, refer to https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/11828/11763

Basic idea:

it is a discriminative model for nonparameteric clustering, which leverages dirchlet process and maximum margin clustering

#Gibbs sampling:

  1. it uses dirichlet process as the prior to generate the number of clusters
  2. the likelihood is from maximum margin model
  3. inference is done based on posterior probability

Learning

it is based on maximum margin online learning to update component parameters

Parameter Setting:

Check the parameter C in dpmm_mmc.m, which balances the contribution from prior and likelihood.

#Demo demodpmm_mmc.m

#Reference Maximum Margin Dirichlet Process Mixtures for Clustering, G. Chen, H. Zhang and C. Xiong. AAAI Conference on Artificial Intelligence (AAAI 2016).