Results 21 to 30 of about 6,366,089 (278)
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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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +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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Learning a manifold of fonts [PDF]
ACM Transactions on Graphics, 2014 The design and manipulation of typefaces and fonts is an area requiring substantial expertise; it can take many years of study to become a proficient typographer. At the same time, the use of typefaces is ubiquitous; there are many users who, while not experts, would like to be more involved in tweaking or changing existing fonts without suffering the ...
Neill D. F. Campbell, Jan Kautz
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Manifold-like matchbox manifolds
, 2019 A matchbox manifold is a generalized lamination, which is a continuum whose arc-components 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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Probabilistic learning on manifolds
Foundations of Data Science, 2020 41 pages, 4 ...
Soize, Christian, Ghanem, Roger
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