Results 11 to 20 of about 28,509 (255)
IEEE Access, 2021 In recent years, a variety of supervised manifold learning techniques have been proposed to outperform their unsupervised alternative versions in terms of classification accuracy and data structure capturing. Some dissimilarity measures have been used in
Laureta Hajderanj +2 more
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eLife, 2022 Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low ...
Corson Areshenkoff +5 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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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 for machine learning assurance [PDF]
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results, 2020 The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system ...
Taejoon Byun, Sanjai Rayadurgam
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Learning a Manifold as an Atlas [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining ...
Nikolaos Pitelis +2 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.
Kanwal K. Bhatia +5 more
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Manifold Learning via Manifold Deflation
CoRR, 2020 Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as vulnerability to noise, repeated eigendirections, holes in convex bodies, and boundary bias.
Daniel Ting, Michael I. Jordan
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