PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Clustering of Processes
Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary and Philippe Preux
In: AISTATS 2012, April 21-23, 2012, La Palma, Canary Islands, Spain.


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).

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:9487
Deposited By:Daniil Ryabko
Deposited On:16 March 2012