Results 261 to 270 of about 5,853,511 (292)

Semisupervised Feature Selection via Structured Manifold Learning

IEEE Transactions on Cybernetics, 2021
Recently, semisupervised feature selection has gained more attention in many real applications due to the high cost of obtaining labeled data. However, existing methods cannot solve the “multimodality” problem that samples in some classes lie in several ...
Xiaojun Chen, Renjie Chen, Qingyao Wu, F. Nie, Min Yang, Rui Mao   +5 more
semanticscholar   +1 more source

Geometric Manifold Learning

IEEE Signal Processing Magazine, 2011
We present algorithms for analyzing massive and high-dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated.
Jamshidi, Arta, Kirby, Michael, Broomhead, Dave   +2 more
openaire   +2 more sources

Global Manifold Learning for Interactive Image Segmentation

IEEE transactions on multimedia, 2021
This paper presents an interactive image segmen-tation algorithm, in which the segmentation problem is formulated as a global manifold learning process. Based on the principle that the label of each element depends on the influence of all the elements in
Tao Wang, Zexuan Ji, Jian Yang, Quansen Sun, Peng Fu   +4 more
semanticscholar   +1 more source

Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Tong, Lin, Hongbin, Zha
openaire   +2 more sources

Image reconstruction by domain-transform manifold learning

Nature, 2017
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio ...
Bo Zhu, Jeremiah Zhe Liu, B. Rosen, M. Rosen   +3 more
semanticscholar   +1 more source

Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface

IEEE journal of biomedical and health informatics
Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is
Haitao Yu, Fanyi Zeng, Dongliang Liu, Jiang Wang, Jialin Liu   +4 more
semanticscholar   +1 more source

Manifold Regularized Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems, 2018
This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method.
Hongliang Li, Derong Liu, Ding Wang
openaire   +2 more sources

Spatial-Neighborhood Manifold Learning for Nondestructive Testing of Defects in Polymer Composites

IEEE Transactions on Industrial Informatics, 2020
The subspace learning (dimensionality reduction) algorithms have played an important role in the analysis of thermographic data: a key step in infrared thermography-based nondestructive testing of subsurface defects in composite materials.
Yi Liu, Kaixin Liu, Jian-guo Yang, Yuan Yao   +3 more
semanticscholar   +1 more source

Learning Invariance Manifolds

Neurocomputing, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Manifold Learning in Protein Interactomes

Journal of Computational Biology, 2011
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the ...
MARRAS, ELISABETTA, Travaglione A, Capobianco E.   +2 more
openaire   +3 more sources