Results 131 to 140 of about 201,485 (174)

Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer’s Disease

, 2021
Gunawardena R, Sarrigiannis PG, Blackburn DJ, He F.   +3 more
europepmc   +1 more source

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies. [PDF]

Aging (Albany NY)
Xiong Y, Li S, Bai Y, Chen T, Sun W, Chen L, Yu J, Sun L, Li C, Wang J, Wu B.   +10 more
europepmc   +1 more source

Manifold Learning in Medical Imaging

, 2019
openaire   +4 more sources

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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

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

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

Learning Invariance Manifolds

Neurocomputing, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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