- December 04, 2014
- Helen Wood Hall - 1W501 Classroom
- Event Sponsor:
- Department of Biostatistics and Computational Biology , University of Rochester
- Hongyu Miao
Department of Biostatistics and Computational BiologyUniversity of Rochester School of Medicine and Dentistry2014 Fall ColloquiumHongyu Miao, PhDAssistant ProfessorDepartment of Biostatistics and Computational BiologyUniversity of Rochester“Clustering Tree-structured Data on Manifold”Thursday, December 4, 20143:30 P.M. – 5:00 p.m.Helen Wood Hall – 1W501 Classroom
Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of Euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illustrate its efficiency and accuracy.