Learning low-dimensional nonlinear structures from high-dimensional noisy data: An integral operator approach
Learning low-dimensional nonlinear structures from high-dimensional noisy data: An integral operator approach
We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from noisy and high-dimensional observations, where the data sets are assumed to be sampled from a nonlinear manifold model and corrupted by high-dimensional noise. The algorithm employs an adaptive bandwidth selection procedure which does not rely on prior knowledge …