Results 231 to 240 of about 28,509 (255)

Learning to Optimize on Riemannian Manifolds

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Many 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, Yuwei Wu 0001, Xiaomeng Fan, Mehrtash Harandi, Yunde Jia   +4 more
openaire   +2 more sources

Manifold Learning of COPD

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, Jamie R. McClelland, Joseph Jacob, John R. Hurst, David J. Hawkes   +4 more
openaire   +2 more sources

Manifold-Based Learning and Synthesis

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
This 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
openaire   +2 more sources

Continuum Isomap for manifold learnings

Computational Statistics & Data Analysis, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongyuan Zha, Zhenyue Zhang
openaire   +2 more sources

MANIFOLD LEARNING FOR ROBOT NAVIGATION

International Journal of Neural Systems, 2006
In this paper we introduce methods to build a SOM that can be used as an isometric map for mobile robots. That is, given a dataset of sensor readings collected at points uniformly distributed with respect to the ground, we wish to build a SOM whose neurons (prototype vectors in sensor space) correspond to points uniformly distributed on the ground ...
Keeratipranon, Narongdech, Maire, Frederic, Huang, Henry   +2 more
openaire   +3 more sources

Learning Manifolds in Forensic Data

2006
Chemical 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, Anne-Laure Terrettaz-Zufferey, Mikhail F. Kanevski, Pierre Esseiva, Olivier Ribaux   +4 more
openaire   +1 more source

Statistical Learning via Manifold Learning

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
A 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, Alexander P. Kuleshov, Yury Yanovich   +2 more
openaire   +1 more source

Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image

IEEE Transactions on Cybernetics, 2021
Yule Duan, Zhengying Li, Hong Huang
exaly  

Multi-task manifold learning for small sample size datasets

Neurocomputing, 2022
Tetsuo Furukawa, Hideaki Ishibashi
exaly  

Label Distribution Learning by Exploiting Label Distribution Manifold

IEEE Transactions on Neural Networks and Learning Systems, 2023
Xin Geng, Jing Wang
exaly