Quotient space model based hierarchical machine learning

We proposed a quotient space based model that can represent the world at different granularities and can be used to handle problems hierarchically. The model can be used in two different ways: top-down deduction and bottom-up induction. In this paper, we will discuss the quotient space model based bottom-up induction, i.e., hierarchical learning. Some approaches for learning the structural knowledge from data are presented. The main advantage of hierarchical induction is its efficiency, that is, the whole structure of data can be abstracted at once.