—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
— Redistricting is the process of dividing a geographic area into districts or zones. This process has been considered in the past as a problem that is computationally too comple...
This work examines under what conditions compression methodologies can retain the outcome of clustering operations. We focus on the popular k-Means clustering algorithm and we dem...
Deepak S. Turaga, Michail Vlachos, Olivier Versche...
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
—With the vast increase in collection and storage of data, the problem of data summarization is most critical for effective data management. Since much of this data is categorica...
—We propose a novel representative based subgraph mining model. A series of standards and methods are proposed to select invariants. Patterns are mapped into invariant vectors in...
Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereb...
Peter Boyen, Frank Neven, Dries Van Dyck, Aalt-Jan...
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since th...
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a f...