Results 11 to 20 of about 36,894 (295)
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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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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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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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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Galaxy Evolution with Manifold Learning [PDF]
EntropyMatter in the early Universe was nearly uniform, and galaxies emerged through the gravitational growth of small primordial density fluctuations. Astrophysics has been trying to unveil the complex physical phenomena that have caused the formation and ...
Tsutomu T. Takeuchi +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 ...
Bhalerao, Abhir +5 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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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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Remote Sensing, 2019 In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification.
Jingliang Hu +3 more
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