Robust Kernel Sparse Subspace Clustering. (arXiv:2401.17035v1 [cs.LG])

Kernel methods are applied to many problems in pattern recognition, including
subspace clustering (SC). That way, nonlinear problems in the input data space
become linear in mapped high-dimensional feature space. Thereby,
computationally tractable nonlinear algorithms are enabled through implicit
mapping by the virtue of kernel trick. However, kernelization of linear
algorithms is possible only if square of the Froebenious norm of the error term
is used in related optimization problem. That, however, implies normal
distribution of the error. That is …

algorithms arxiv become clustering cs.lg data feature kernel linear mapped mapping pattern recognition recognition robust space through tractable trick