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Manifold Learning in Protein Interactomes
Journal of Computational Biology, 2011Many 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 +2 more
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Learning to Optimize on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Many learning tasks are modeled as optimization problems with nonlinear constraints, such as principal component analysis and fitting a Gaussian mixture model. A popular way to solve such problems is resorting to Riemannian optimization algorithms, which yet heavily rely on both human involvement and expert knowledge about Riemannian manifolds. In this
Zhi Gao 0002 +4 more
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2017
Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies.
Felix J. S. Bragman +4 more
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Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies.
Felix J. S. Bragman +4 more
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Manifold-Based Learning and Synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009This paper proposes a new approach to analyze high-dimensional data set using low-dimensional manifold. This manifold-based approach provides a unified formulation for both learning from and synthesis back to the input space. The manifold learning method desires to solve two problems in many existing algorithms.
Dong Huang, Zhang Yi 0001, Xiaorong Pu
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Continuum Isomap for manifold learnings
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongyuan Zha, Zhenyue Zhang
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Learning Manifolds in Forensic Data
2006Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in chemical drug profiling, a recent field in the crime mapping community. The data has been collected using gas chromatography analysis.
Frédéric Ratle +4 more
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Statistical Learning via Manifold Learning
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015A new geometrically motivated method is proposed for solving the non-linear regression task consisting in constructing a predictive function which estimates an unknown smooth mapping f from q-dimensional inputs to m-dimensional outputs based on a given 'input-output' training pairs. The unknown mapping f determines q-dimensional Regression manifold M(f)
Alexander V. Bernstein +2 more
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Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image
IEEE Transactions on Cybernetics, 2021Yule Duan, Hong Huang, Zhengying Li
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Multi-task manifold learning for small sample size datasets
Neurocomputing, 2022Hideaki Ishibashi, Tetsuo Furukawa
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Label Distribution Learning by Exploiting Label Distribution Manifold
IEEE Transactions on Neural Networks and Learning Systems, 2023Jing Wang, Xin Geng
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