Results 31 to 40 of about 201,485 (174)
Tangent space estimation for smooth embeddings of Riemannian manifolds [PDF]
, 2012 Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require the estimation of the tangent space of the manifold at
Frossard, Pascal +2 more
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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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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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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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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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Manifold Learning by Graduated Optimization [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011 We present an algorithm for manifold learning called manifold sculpting , which utilizes graduated optimization to seek an accurate manifold embedding. An empirical analysis across a wide range of manifold problems indicates that manifold sculpting yields more accurate results than a number of existing algorithms, including Isomap, locally linear ...
M, Gashler, D, Ventura, T, Martinez
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Manifold for machine learning assurance [PDF]
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results, 2020 The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system ...
Byun, Taejoon, Rayadurgam, Sanjai
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Learning Generative Models across Incomparable Spaces
, 2019 Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety.
Alvarez-Melis, David +3 more
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Electronic Proceedings in Theoretical Computer Science, 2022 In Proceedings ACT 2021, arXiv:2211 ...
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