Results 41 to 50 of about 6,366,089 (278)
, 2010 Mathematical formulation of certain natural phenomena exhibits group structure on topological spaces that resemble the Euclidean space only on a small enough scale, which prevents incorporation of conventional inference methods that require global vector norms.
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Manifold Learning of COPD [PDF]
, 2017 Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies.
Mingliang Wang +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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Methodology for automatic recovering of 3D partitions from unstitched faces of non-manifold CAD models [PDF]
, 2013 Data exchanges between different software are currently used in industry to speed up the preparation of digital prototypes for Finite Element Analysis (FEA).
PERNOT, Jean-Philippe +1 more
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Learning on manifolds without manifold learning
Neural NetworksFunction approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampled from an unknown submanifold of a high dimensional Euclidean space.
Hrushikesh N. Mhaskar, Ryan O'Dowd
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CoRR, 2019 Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Max Aalto, Nakul Verma
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CoRR, 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.
Shufei Zhang +3 more
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A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
Entropy, 2018 Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge.
Xulun Ye, Jieyu Zhao, Yu Chen
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Graph Regularized Variational Ladder Networks for Semi-Supervised Learning
IEEE Access, 2020 To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi ...
Cong Hu, Xiao-Ning Song
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Joint Characterization of Sentinel-2 Reflectance: Insights from Manifold Learning
Remote Sensing, 2022 Most applications of multispectral imaging are explicitly or implicitly dependent on the dimensionality and topology of the spectral mixing space. Mixing space characterization refers to the identification of salient properties of the set of pixel ...
Daniel Sousa, Christopher Small
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