Results 11 to 20 of about 201,485 (174)
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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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
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Manifold Learning with Arbitrary Norms [PDF]
Journal of Fourier Analysis and Applications, 2021 Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many of these methods are graph-based: they associate a vertex with each data point and a weighted edge with each pair. Existing theory shows that the Laplacian matrix of the graph
Joe Kileel +3 more
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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
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Out-of-sample generalizations for supervised manifold learning for classification [PDF]
, 2015 Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the ...
Guillemot, Christine, Vural, Elif
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Probabilistic learning on manifolds
Foundations of Data Science, 2020 41 pages, 4 ...
Soize, Christian, Ghanem, Roger
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The Structure Transfer Machine Theory and Applications [PDF]
, 2019 Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to ...
Han, Jungong +5 more
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Remote Sensing, 2020 Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s ...
Chu He +3 more
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Face manifold: manifold learning for synthetic face generation
Multimedia Tools and Applications, 2023 Face is one of the most important things for communication with the world around us. It also forms our identity and expressions. Estimating the face structure is a fundamental task in computer vision with applications in different areas such as face recognition and medical surgeries.
Kimia Dinashi +2 more
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