Dimensionality-reduced subspace clustering

25 Jul 2015Reinhard HeckelMichael TschannenHelmut Bölcskei

Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dimensions are all unknown. In practice one may have access to dimensionality-reduced observations of the data only, resulting, e.g., from undersampling due to complexity and speed constraints on the acquisition device or mechanism... (read more)

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