Online clustering of processes

The problem of online clustering is consid-ered 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 time-step is either a continuation of some previ-ously received sequence or a new sequence. The dependence between the sequences can be arbitrary. No parametric or indepen-dence assumptions are made; the only as-sumption 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 consis-tency). The performance of the proposed al-gorithm is evaluated on simulated data, as well as on real datasets (motion classifica-tion).