The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
We introduce a new method for data clustering based on a particular Gaussian mixture model (GMM). Each cluster of data, modeled as a GMM into an input space, is interpreted as a hy...
We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of o...
Abstract. Semi-supervised clustering models, that incorporate user provided constraints to yield meaningful clusters, have recently become a popular area of research. In this paper...
Abstract. The high dimensionality of the data generated by social networks has been a big challenge for researchers. In order to solve the problems associated with this phenomenon,...