Results 21 to 30 of about 5,853,511 (292)
Adaptive Manifold Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization.
Zhenyue, Zhang +2 more
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Manifold Learning Benefits GANs [PDF]
Computer Vision and Pattern Recognition, 2021 In this paper11Code: https://qithub.com/MaxwellYaoNi/LCSAGAN., we improve Generative Adversarial Net-works by incorporating a manifold learning step into the discriminator.
Yao Ni +3 more
semanticscholar +1 more source
Using manifold learning for atlas selection in multi-atlas segmentation. [PDF]
PLoS ONE, 2013 Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has
Albert K Hoang Duc +7 more
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Label Propagation Algorithm for Intersecting Multi-manifolds Clustering [PDF]
Jisuanji gongcheng, 2023 The classical manifold learning algorithm assumes that the sample data is located on a high-dimensional single manifold；however，the real data in real life is located on a high-dimensional multi-manifold，and these data often overlap，resulting in poor ...
GAO Xiaofang, YUAN Yuliang, WEN Jing, BAI Xuefei
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Deep Manifold Learning for Dynamic MR Imaging [PDF]
IEEE Transactions on Computational Imaging, 2021 Recently, low-dimensional manifold regularization has been recognized as a competitive method for accelerated cardiac MRI, due to its ability to capture temporal correlations. However, existing methods have not been performed with the nonlinear structure
Ziwen Ke +7 more
semanticscholar +1 more source
Comptes Rendus. Mécanique, 2020 The present work aims at analyzing issues related to the data manifold dimensionality. The interest of the study is twofold: (i) first, when too many measurable variables are considered, manifold learning is expected to extract useless variables; (ii ...
Ibanez, Ruben +3 more
doaj +1 more source
Hierarchical Manifold Learning [PDF]
, 2012 We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels.
Bhatia, Kanwal K +5 more
openaire +3 more sources
UMAP: Uniform Manifold Approximation and Projection
Journal of Open Source Software, 2018 Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. UMAP has a rigorous mathematical foundation, but is simple
Leland McInnes +3 more
semanticscholar +1 more source
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction [PDF]
, 2010 It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square ...
A D’aspremont +40 more
core +1 more source
Probabilistic learning on manifolds
Foundations of Data Science, 2020 41 pages, 4 ...
Soize, Christian, Ghanem, Roger
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