no code implementations • 28 Feb 2022 • Xiucai Ding, Rong Ma
We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dimensional manifold and corrupted by high-dimensional noise.
no code implementations • 22 Nov 2021 • Xiucai Ding, Hau-Tieng Wu
It turns out that both the asymptotic limits and convergence rates depend on the signal-to-noise ratio (SNR) of each sensor and selected bandwidths.
no code implementations • 21 Nov 2020 • Xiucai Ding, Hau-Tieng Wu
We systematically study the spectrum of kernel-based graph Laplacian (GL) constructed from high-dimensional and noisy random point cloud in the nonnull setup.
no code implementations • 24 Oct 2018 • Xiucai Ding, Qiang Sun
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning.