Results 141 to 150 of about 201,485 (174)
Some of the next articles are maybe not open access.
Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang, Sridhar Mahadevan
openaire +1 more source
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang, Sridhar Mahadevan
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
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 Bernstein +2 more
openaire +1 more source
2012
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-
Mateus, Diana +4 more
openaire +2 more sources
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-
Mateus, Diana +4 more
openaire +2 more sources
Continuum Isomap for manifold learnings
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zha, Hongyuan, Zhang, Zhenyue
openaire +2 more sources
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, Xiaorong, Pu
openaire +2 more sources
MANIFOLD LEARNING FOR ROBOT NAVIGATION
International Journal of Neural Systems, 2006In 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 +2 more
openaire +3 more sources
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.
Ratle Frederic +4 more
openaire +1 more source
Similarity Learning of Manifold Data
IEEE Transactions on Cybernetics, 2015Without constructing adjacency graph for neighborhood, we propose a method to learn similarity among sample points of manifold in Laplacian embedding (LE) based on adding constraints of linear reconstruction and least absolute shrinkage and selection operator type minimization. Two algorithms and corresponding analyses are presented to learn similarity
null Si-Bao Chen +2 more
openaire +2 more sources