A statistical framework for online learning using adjustable model selection criteria

• An online learning framework for generalized inverted Dirichlet mixtures is proposed.
• The proposed statistical model takes into account simultaneously user׳s perception and the dynamic nature of real-world data.
• The model is applied to the challenging problem of visual objects classification.

Model-based approaches have been for long an effective method to model data and classify it. Recently they have been used to model users interactions with a given system in order to satisfy their needs through adequate responses. The semantic gap between the system and the user perception for the data makes this modeling hard to be designed based on the features space only. Indeed the user intervention is somehow needed to inform the system how the data should be perceived according to some ontology and hierarchy when new data are introduced to the model. Such a task is challenging as the system should learn how to establish the update according to the user perception and representation of the data. In this work, we propose a new methodology to update a mixture model based on the generalized inverted Dirichlet distribution, that takes into account simultaneously user׳s perception and the dynamic nature of real-world data. Experiments on synthetic data as well as real data generated from a challenging application namely visual objects classification indicate that the proposed approach has merits and provides promising results.