Zobrazeno 1 - 10
of 8 626
pro vyhledávání: '"diffusion maps"'
Autor:
Bo, Wenyu, Meilă, Marina
Under a set of assumptions on a family of submanifolds $\subset {\mathbb R}^D$, we derive a series of geometric properties that remain valid after finite-dimensional and almost isometric Diffusion Maps (DM), including almost uniform density, finite p
Externí odkaz:
http://arxiv.org/abs/2412.03992
Publikováno v:
Physics of Fluids 36, 105187 (2024)
We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from steady sy
Externí odkaz:
http://arxiv.org/abs/2408.02630
Autor:
Rydzewski, Jakub
Publikováno v:
J. Phys. Chem. Lett. 2023, 14, 11, 2778-2783
Constructing reduced representations of high-dimensional systems is a fundamental problem in physical chemistry. Many unsupervised machine learning methods can automatically find such low-dimensional representations. However, an often overlooked prob
Externí odkaz:
http://arxiv.org/abs/2404.02639
Akademický článek
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Akademický článek
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Dimensional reduction techniques have long been used to visualize the structure and geometry of high dimensional data. However, most widely used techniques are difficult to interpret due to nonlinearities and opaque optimization processes. Here we pr
Externí odkaz:
http://arxiv.org/abs/2401.03095
Autor:
Hildebrant, Todd
This paper explores the application diffusion maps as graph shift operators in understanding the underlying geometry of graph signals. The study evaluates the improvements in graph learning when using diffusion map generated filters to the Markov Var
Externí odkaz:
http://arxiv.org/abs/2312.14758
We obtain asymptotically sharp error estimates for the consistency error of the Target Measure Diffusion map (TMDmap) (Banisch et al. 2020), a variant of diffusion maps featuring importance sampling and hence allowing input data drawn from an arbitra
Externí odkaz:
http://arxiv.org/abs/2312.14418
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 1, Pp 1554-1572 (2024)
Graph convolutional networks (GCN) have been widely utilized in Alzheimer's disease (AD) classification research due to its ability to automatically learn robust and powerful feature representations. Inter-patient relationships are effectively captur
Externí odkaz:
https://doaj.org/article/85a6a5a478ed4c54a66aef3ae4dd81f9
Autor:
Goo, June Moh
This research proposes a model to predict the location of the most deprived areas in a city using data from the census. Census data is very high-dimensional and needs to be simplified. We use the diffusion map algorithm to reduce dimensionality and f
Externí odkaz:
http://arxiv.org/abs/2312.09830