Final published version
Research output: Contribution to Journal/Magazine › Conference article › peer-review
Research output: Contribution to Journal/Magazine › Conference article › peer-review
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TY - JOUR
T1 - Online clustering of processes
AU - Khaleghi, Azadeh
AU - Ryabko, Daniil
AU - Mary, Jérémie
AU - Preux, Philippe
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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).
AB - 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).
M3 - Conference article
AN - SCOPUS:84877745287
VL - 22
SP - 601
EP - 609
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
SN - 1938-7228
T2 - 15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012
Y2 - 21 April 2012 through 23 April 2012
ER -