Results 11 to 20 of about 6,366,089 (278)
Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures [PDF]
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 in statistical tasks
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 Many tasks of data analysis deal with high-dimensional data, and curse of dimensionality is an obstacle to the use of many methods for their solving.
A.V. Bernstein
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques
IEEE Access, 2023 Manifold learning techniques aim to the non-linear dimension reduction of data. Dimension reduction is the field of interest and demand of many data analysts and is widely used in computer vision, image processing, pattern recognition, neural networks ...
Amna Ashraf +2 more
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Unsupervised Learning of Shape Manifolds [PDF]
Procedings of the British Machine Vision Conference 2007, 2007 Classical shape analysis methods use principal component analysis to reduce the dimensionality of shape spaces. The basic assumption behind these methods is that the subspace corresponding to the major modes of variation for a particular class of shapes is linearised. This may not necessarily be the case in practice.
Nasir M. Rajpoot +2 more
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Backdoors on Manifold Learning [PDF]
Proceedings of the 2024 ACM Workshop on Wireless Security and Machine LearningWiSec ...
Christina Kreza +4 more
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Manifold aligned density estimation [PDF]
, 2010 With the advent of the information technology, the amount of data we are facing today is growing in both the scale and the dimensionality dramatically. It thus raises new challenges for some traditional machine learning tasks.
Wang, Xiaoxia
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Manifold-like Matchbox Manifolds [PDF]
, 2017 A matchbox manifold is a generalized lamination, which is a continuum whose arccomponents define the leaves of a foliation of the space. The main result of this paper implies that a matchbox manifold which is manifold-like must be homeomorphic to a weak ...
Olga Lukina (7780643) +2 more
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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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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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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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