Handling uncertain data in subspace detection
A general solution for detecting data alignments (subspaces) in unordered multidimensional data. Our approach can handle both exact data as well as data containing Gaussian distributed uncertainties. Our approach allows the concurrent detection of different types of data alignments. Our approach allows the detection of data alignments in heterogeneous datasets.
uncertain data subspace detection hough transform shape detection grassmannian geometric algebra parameter space