Final published version
Research output: Contribution to Journal/Magazine › Conference article › peer-review
<mark>Journal publication date</mark> | 1/01/2012 |
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<mark>Journal</mark> | Proceedings of Machine Learning Research |
Volume | 22 |
Number of pages | 9 |
Pages (from-to) | 601-609 |
Publication Status | Published |
<mark>Original language</mark> | English |
Event | 15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 - La Palma, Spain Duration: 21/04/2012 → 23/04/2012 |
Conference | 15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 |
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Country/Territory | Spain |
City | La Palma |
Period | 21/04/12 → 23/04/12 |
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).