In this project, we aim to develop a solution for the problem of finding multiple clusterings of data points residing in different subspaces on a data set. We refer to such a problem as multifaceted subspace clustering. The solution will be based on probabilistic models due to their sound statistical basis. The proposed models will contain multiple discrete latent variables to represent multiple clusterings along different facets. To represent the local subspaces in each clustering, we will consider model structures based on conventional dimension reduction techniques. To represent the probabilistic relationships among the clusterings, we will consider two kinds of network structures with varying levels of complexity. We will develop inference algorithms as well as parameter and structure learning algorithms for those variants of models. The model variants will be evaluated and compared on various kinds of data sets where multiple meaningful clusterings are known to exist.
|Effective start/end date||01/01/20 → 31/12/21|