Online clustering of processes

Khaleghi, Azadeh and Ryabko, Daniil and Mary, Jérémie and Preux, Philippe (2012) Online clustering of processes. Proceedings of Machine Learning Research, 22. pp. 601-609. ISSN 1938-7228

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The problem of online clustering is considered in the case where each data point is a sequence generated by a stationary ergodic process. Data arrive in an online fashion so that the sample received at every timestep is either a continuation of some previously received sequence or a new sequence. The dependence between the sequences can be arbitrary. No parametric or independence assumptions are made; the only assumption is that the marginal distribution of each sequence is stationary and ergodic. A novel, computationally efficient algorithm is proposed and is shown to be asymptotically consistent (under a natural notion of consistency). The performance of the proposed algorithm is evaluated on simulated data, as well as on real datasets (motion classification).

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of Machine Learning Research
Uncontrolled Keywords:
?? softwarecontrol and systems engineeringstatistics and probabilityartificial intelligence ??
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22 Jun 2019 09:16
Last Modified:
15 Jul 2024 19:29