Results 71 to 80 of about 201,485 (174)
Learning a Robust Local Manifold Representation for Hyperspectral Dimensionality Reduction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 Local manifold learning has been successfully applied to hyperspectral dimensionality reduction in order to embed nonlinear and nonconvex manifolds in the data.
Danfeng Hong +2 more
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques
IEEE AccessManifold 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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Türk Kardiyoloji Derneği Arşivi, 2019 Objective: Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid ...
Fereshteh Yousefi Rizi +2 more
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This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using ...
David Ryckelynck +2 more
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, 2018 Recently proposed adversarial training methods show the robustness to both adversarial and original examples and achieve state-of-the-art results in supervised and semi-supervised learning. All the existing adversarial training methods consider only how the worst perturbed examples (i.e., adversarial examples) could affect the model output.
Zhang, Shufei +3 more
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Regularized manifold information extreme learning machine
Tongxin xuebao, 2016 By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed.
De-shan LIU, Yong-he CHU, De-qin YAN
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Multi-view data visualisation via manifold learning [PDF]
PeerJ Computer ScienceNon-linear dimensionality reduction can be performed by manifold learning approaches, such as stochastic neighbour embedding (SNE), locally linear embedding (LLE) and isometric feature mapping (ISOMAP).
Theodoulos Rodosthenous +2 more
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Alignment of vector fields on manifolds via contraction mappings
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 According to the manifold hypothesis, high-dimensional data can be viewed and meaningfully represented as a lower-dimensional manifold embedded in a higher dimensional feature space. Manifold learning is a part of machine learning where an intrinsic data
O.N. Kachan +2 more
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Improvement of Supervised Shape Retrieval by Learning the Manifold Space
International Journal of Information and Communication Technology Research, 2012 Manifold learning is the technique that aims for finding a constructive way to embed the data from a highdimensional space into a low-dimensional one based on non-linear approaches.
Mohammad Ali Zare Chahooki +1 more
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Deep Nets for Local Manifold Learning
Frontiers in Applied Mathematics and Statistics, 2018 The problem of extending a function f defined on a training data C on an unknown manifold 𝕏 to the entire manifold and a tubular neighborhood of this manifold is considered in this paper. For 𝕏 embedded in a high dimensional ambient Euclidean space ℝD, a
Charles K. Chui, Hrushikesh N. Mhaskar
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