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Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
Theodoros Tsiligkaridis · Theodoros Tsiligkaridis · Keith Forsythe

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #100 #None

We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, in which hyperparameters are recursively updated as a function of the streaming data assuming conjugate priors. Motivated by large-sample asymptotics, we propose a noveladaptive low-complexity design for the Dirichlet process concentration parameter and show that the number of classes grow at most at a logarithmic rate. We further prove that in the large-sample limit, the conditional likelihood and datapredictive distribution become asymptotically Gaussian. We demonstrate through experiments on synthetic and real data sets that our approach is superior to otheronline state-of-the-art methods.

Author Information

Theodoros Tsiligkaridis (MIT Lincoln Laboratory)
Theodoros Tsiligkaridis (MIT Lincoln Laboratory)
Keith Forsythe (MIT Lincoln Laboratory)