Contagion Dynamics for Manifold Learning [PDF]
Frontiers in Big Data, 2022 Contagion maps exploit activation times in threshold contagions to assign vectors in high-dimensional Euclidean space to the nodes of a network. A point cloud that is the image of a contagion map reflects both the structure underlying the network and the
Barbara I. Mahler
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Revealing hidden patterns in deep neural network feature space continuum via manifold learning. [PDF]
Nat Commun, 2023 Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance
Islam MT +8 more
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Discovering conservation laws using optimal transport and manifold learning. [PDF]
Nat Commun, 2023 Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to ...
Lu PY, Dangovski R, Soljačić M.
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Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations. [PDF]
J Chem Theory Comput, 2022 Enhanced sampling methods are indispensable in computational chemistry and physics, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced
Rydzewski J +3 more
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Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites. [PDF]
Polymers (Basel), 2022 Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials.
Liu K +5 more
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Multi-Manifold Learning Fault Diagnosis Method Based on Adaptive Domain Selection and Maximum Manifold Edge [PDF]
SensorsThe vibration signal of rotating machinery is usually nonlinear and non-stationary, and the feature set has information redundancy. Therefore, a high-dimensional feature reduction method based on multi-manifold learning is proposed for rotating machinery
Ling Zhao, Jiawei Ding, Pan Li, Xin Chi
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Comparison of manifold learning algorithms for identifying geochemical anomalies associated with copper mineralization [PDF]
Scientific ReportsThe Baiyin district, situated within the northern Qilian orogenic belt, hosts the largest concentration of copper mineral resources in Gansu Province, Northwestern China. Geochemical anomaly patterns are crucial indicators for mineral exploration in this
Yuwen Min +5 more
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Hierarchical simplicial manifold learning. [PDF]
PNAS NexusAbstract Learning global structures, i.e. topological properties, inherent in complex data is an essential yet challenging task that spans across various scientific and engineering disciplines. A fundamental approach is to extract local data representations and use them to assemble the global structure.
Zhang W, Shih YH, Li JS.
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A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning [PDF]
SensorsWith the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects.
Wenhui Song +5 more
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Manifold Learning with Arbitrary Norms [PDF]
Journal of Fourier Analysis and Applications, 2020 Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality.
Joe Kileel +3 more
semanticscholar +4 more sources