20141127
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 unionofsubspaces (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 bisparsity 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 SeminarTechnische Universität Darmstadt
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