Exact(45)
For diffusion maps we develop a new inverse map approximation.
Thompson refers to his diffusion maps as "pictures of mental speed". Previous research has repeatedly linked IQ to processing speed, and other studies show that processing speed in turn is tightly linked to the quality of one's white matter.
In this paper, we use kernel PCA and diffusion maps to construct GP emulators for very high-dimensional output spaces arising from PDE model simulations.
The diffusion maps framework is a kernel-based method for manifold learning and data analysis that models a Markovian process over data.
We introduce a theory of local kernels, which generalize the kernels used in the standard diffusion maps construction of nonparametric modeling.
We show that diffusion maps, with affinity kernels based on the Kullback Leibler divergence between the local statistics of samples, allow for efficient approximation of pairwise geodesic distances.
Similar(15)
Diffusion map is a nonlinear eigen-technique that captures that geometry by propagating local neighborhood information through a Markov process.
a, Diffusion map of peripheral blood mononuclear cells (PBMCs) sampled from n = 28 patients at baseline, the time of surgery and on-treatment.
We compare random projection, principal component analysis and diffusion map for anomaly detection.
The first two methods have intuitive extensions while diffusion map uses the Nyström extension.
Diffusion Map (DM), Local Linear Embedding LLEE) and AutoEncoder (AE) algorithms were used for future extraction.
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