9,035 research outputs found
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
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 novel adaptive 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 data predictive distribution become asymptotically
Gaussian. We demonstrate through experiments on synthetic and real data sets
that our approach is superior to other online state-of-the-art methods.Comment: 25 pages, To appear in Advances in Neural Information Processing
Systems (NIPS) 201
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
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