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Learning a manifold of fonts [PDF]
ACM Transactions on Graphics, 2014 The design and manipulation of typefaces and fonts is an area requiring substantial expertise; it can take many years of study to become a proficient typographer. At the same time, the use of typefaces is ubiquitous; there are many users who, while not experts, would like to be more involved in tweaking or changing existing fonts without suffering the ...
Neill D. F. Campbell, Jan Kautz
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Probabilistic learning on manifolds
Foundations of Data Science, 2020 41 pages, 4 ...
Soize, Christian, Ghanem, Roger
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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +3 more
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Remote Sensing, 2020 Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s ...
Chu He +3 more
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Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning
IEEE Access, 2017 Fault feature can be extracted by traditional manifold learning algorithms, which construct neighborhood graphs by Euclidean distance (ED). It is difficult to get an excellent dimensionality reduction result when processed data has strong correlations ...
Beibei Yao +3 more
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An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals
IEEE Access, 2020 Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation.
Lorenzo Manoni +2 more
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Manifold Learning and Nonlinear Homogenization
Multiscale Modeling & Simulation, 2022 We describe an efficient domain decomposition-based framework for nonlinear multiscale PDE problems. The framework is inspired by manifold learning techniques and exploits the tangent spaces spanned by the nearest neighbors to compress local solution manifolds.
Shi Chen 0003 +3 more
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LKLR: A Local Tangent Space-Alignment Kernel Least-Squares Regression Algorithm
Tsinghua Science and Technology, 2019 In the fields of machine learning and data mining, label learning is a nascent area of research, and within this paradigm, there is much room for improving multi-label manifold learning algorithms for high-dimensional data.
Chao Tan, Genlin Ji
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Sensors, 2022 Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible.
Muhammad Zafran Muhammad Zaly Shah +4 more
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