DMCA
Sequential clustering with particle filters: Extimating the number of clusters from data (2005)
| Venue: | 7th International Conference on Information Fusion (FUSION |
| Citations: | 11 - 6 self |
BibTeX
@INPROCEEDINGS{Schubert05sequentialclustering,
author = {Johan Schubert},
title = {Sequential clustering with particle filters: Extimating the number of clusters from data},
booktitle = {7th International Conference on Information Fusion (FUSION},
year = {2005}
}
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Abstract
Abstract- In this paper we develop a particle filtering approach for grouping observations into an unspecified number of clusters. Each cluster corresponds to a potential target from which the observations originate. A potential clustering with a specified number of clusters is represented by an association hypothesis. Whenever a new report arrives, a posterior distribution over all hypotheses is iteratively calculated from a prior distribution, an update model and a likelihood function. The update model is based on an association probability for clusters given the probability of false detection and a derived probability of an unobserved target. The likelihood of each hypothesis is derived from a cost value of associating the current report with its corresponding cluster according to the hypothesis. A set of hypotheses is maintained by Monte Carlo sampling. In this case, the state-space, i.e., the space of all hypotheses, is discrete with a linearly growing dimensionality over time. To lower the complexity further, hypotheses are combined if their clusters are close to each other in the observation space. Finally, for each time-step, the posterior distribution is projected into a distribution over the number of clusters. Compared to earlier information theoretic approaches for finding the number of clusters this approach does not require a large number of trial clusterings, since it maintains an estimate of the number of clusters along with the cluster configuration.
Keyphrases
particle filter posterior distribution update model monte carlo association hypothesis large number observation space likelihood function potential clustering potential target association probability false detection unobserved target trial clustering specified number unspecified number cluster configuration cost value information theoretic approach derived probability prior distribution current report new report corresponding cluster
