Results 41 to 50 of about 5,853,511 (292)
Adaptive Safe Semi-Supervised Extreme Machine Learning
IEEE Access, 2019 Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples.
Jun Ma, Chao Yuan
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Robust Beamforming for RIS-Aided Communications: Gradient-Based Manifold Meta Learning [PDF]
IEEE Transactions on Wireless CommunicationsReconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways.
Fenghao Zhu +8 more
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A Neural Network Based on SPD Manifold Learning for Skeleton-Based Hand Gesture Recognition [PDF]
Computer Vision and Pattern Recognition, 2019 This paper proposes a new neural network based on SPD manifold learning for skeleton-based hand gesture recognition. Given the stream of hand’s joint positions, our approach combines two aggregation processes on respectively spatial and temporal domains.
X. Nguyen +3 more
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Manifold Learning via Manifold Deflation
, 2020 Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as vulnerability to noise, repeated eigendirections, holes in convex bodies, and boundary bias.
Ting, Daniel, Jordan, Michael I.
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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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Multi-objective genetic programming for manifold learning: balancing quality and dimensionality [PDF]
Genetic Programming and Evolvable Machines, 2020 Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the ...
Andrew Lensen, Mengjie Zhang, Bing Xue
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Curvature-aware manifold learning [PDF]
Pattern Recognition, 2018 Traditional manifold learning algorithms assumed that the embedded manifold is globally or locally isometric to Euclidean space. Under this assumption, they divided manifold into a set of overlapping local patches which are locally isometric to linear subsets of Euclidean space. By analyzing the global or local isometry assumptions it can be shown that
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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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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.
H.N. Mhaskar, Ryan O’Dowd
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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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