Towards Non-Smooth Efficient Modeling of Large Data

Prof. Hamid Krim, North Carolina State University, Raleigh (U.S.A.)

2 Dec 2014, 14:30–15:30; Location: S3|06-257

High dimensional data exhibit distinct properties compared to its low dimensional counterpart; this causes a common performance decrease and a formidable computational cost increase of traditional approaches. Novel methodologies are therefore needed to characterize data in high dimensional spaces.

Considering the parsimonious degrees of freedom of high dimensional data compared to its dimensionality, we study the union-of-subspaces (UoS) model, as a generalization of the linear subspace model. The UoS model preserves the simplicity of the linear subspace model, and enjoys the additional ability to address nonlinear data. We show a sufficient condition to use l1 minimization to reveal the underlying UoS structure, and further propose a bi-sparsity model (RoSure) as an effective algorithm, to recover the given data characterized by the UoS model from errors/corruptions. This framework shows superior performance for a wide range of problems, such as face clustering and video segmentation.

Category: CE Seminar


Technische Universität Darmstadt

Graduate School CE
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D-64293 Darmstadt

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