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Online clustering of processes

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<mark>Journal publication date</mark>1/01/2012
<mark>Journal</mark>Proceedings of Machine Learning Research
Volume22
Number of pages9
Pages (from-to)601-609
Publication statusPublished
Original languageEnglish
Event15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 - La Palma, Spain
Duration: 21/04/201223/04/2012

Conference

Conference15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012
CountrySpain
CityLa Palma
Period21/04/1223/04/12

Abstract

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).