Results 21 to 30 of about 36,894 (295)
Unsupervised manifold learning of collective behavior. [PDF]
PLoS Computational Biology, 2021 Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative ...
Mathew Titus +2 more
doaj +2 more sources
Proceedings of the AAAI Conference on Artificial Intelligence, 2013 Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure.
Wang, Chang, Mahadevan, Sridhar
core +2 more sources
Manifold Learning for Robot Navigation
International Journal of Neural Systems, 2006 In this paper we introduce methods to build a SOM that can be used as an isometric map for mobile robots. That is, given a dataset of sensor readings collected at points uniformly distributed with respect to the ground, we wish to build a SOM whose ...
Keeratipranon, Narongdech +2 more
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Multi-view manifold learning with locality alignment
Pattern Recognition, 2018 © 2018 Elsevier Ltd Manifold learning aims to discover the low dimensional space where the input high dimensional data are embedded by preserving the geometric structure.
Xinge You, Shujian Yu, Chang Xu
exaly +2 more sources
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena. [PDF]
Int Conf Sampl Theory Appl SampTA, 2023 Many techniques in machine learning attempt explicitly or implicitly to infer a low-dimensional manifold structure of an underlying physical phenomenon from measurements without an explicit model of the phenomenon or the measurement apparatus. This paper presents a cautionary tale regarding the discrepancy between the geometry of measurements and the ...
Lederman RR, Toader B.
europepmc +4 more sources
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
doaj +1 more source
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
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source