Results 21 to 30 of about 201,485 (174)
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction [PDF]
, 2010 It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square ...
A D’aspremont +40 more
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Learning deformable shape manifolds [PDF]
Pattern Recognition, 2012 We propose an approach to shape detection of highly deformable shapes in images via manifold learning with regression. Our method does not require shape key points be defined at high contrast image regions, nor do we need an initial estimate of the shape. We only require sufficient representative training data and a rough initial estimate of the object
Samuel, Rivera, Aleix, Martinez
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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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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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Perturbing low dimensional activity manifolds in spiking neuronal networks.
PLoS Computational Biology, 2019 Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative ...
Emil Wärnberg, Arvind Kumar
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Multilabel Learning with Incomplete Using Dual-Manifold Mapping [PDF]
Jisuanji gongchengIn multilabel learning, the classification performance can be improved through the effective use of label correlations. However, owing to the subjectivity of manual tagging and the similarity of label semantics in practical applications, an incomplete ...
XU Zhilei, HUANG Rui
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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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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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