Low Dimensional Manifold Model in Hyperspectral Image Reconstruction

2020 ◽  
pp. 295-317 ◽  
Author(s):  
Wei Zhu ◽  
Zuoqiang Shi ◽  
Stanley Osher
Keyword(s):  
Image Reconstruction ◽  
Hyperspectral Image ◽  
Dimensional Manifold ◽  
Low Dimensional ◽  
Low Dimensional Manifold

scholarly journals Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

2019 ◽  
Vol 9 (10) ◽  
pp. 2161
Author(s):  
Lin He ◽  
Xianjun Chen ◽  
Jun Li ◽  
Xiaofeng Xie
Keyword(s):  
Manifold Learning ◽  
Hyperspectral Image ◽  
Decision Fusion ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Low Dimensional ◽  
Low Dimensional Manifold

Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.

scholarly journals CT image reconstruction on a low dimensional manifold

2019 ◽  
Vol 13 (3) ◽  
pp. 449-460 ◽  
Author(s):  
Wenxiang Cong ◽  
◽  
Ge Wang ◽  
Qingsong Yang ◽  
Jia Li ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Dimensional Manifold ◽  
Ct Image ◽  
Ct Image Reconstruction ◽  
Low Dimensional ◽  
Low Dimensional Manifold

scholarly journals Low-dimensional manifold of actin polymerization dynamics

2017 ◽  
Vol 19 (12) ◽  
pp. 125012 ◽  
Author(s):  
Carlos Floyd ◽  
Christopher Jarzynski ◽  
Garegin Papoian
Keyword(s):  
Actin Polymerization ◽  
Dimensional Manifold ◽  
Low Dimensional ◽  
Low Dimensional Manifold

scholarly journals Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

2020 ◽  
Author(s):  
Wei Guo ◽  
Jie J. Zhang ◽  
Jonathan P. Newman ◽  
Matthew A. Wilson
Keyword(s):  
Hippocampal Neurons ◽  
Dimensional Manifold ◽  
Latent Learning ◽  
Neural Ensembles ◽  
Correlated Activity ◽  
Place Fields ◽  
Internal States ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
The Brain

AbstractLatent learning allows the brain the transform experiences into cognitive maps, a form of implicit memory, without reinforced training. Its mechanism is unclear. We tracked the internal states of the hippocampal neural ensembles and discovered that during latent learning of a spatial map, the state space evolved into a low-dimensional manifold that topologically resembled the physical environment. This process requires repeated experiences and sleep in-between. Further investigations revealed that a subset of hippocampal neurons, instead of rapidly forming place fields in a novel environment, remained weakly tuned but gradually developed correlated activity with other neurons. These ‘weakly spatial’ neurons bond activity of neurons with stronger spatial tuning, linking discrete place fields into a map that supports flexible navigation.

Large-eddy simulation of direct injection natural gas combustion in a heavy-duty truck engine using modified conditional moment closure model with low-dimensional manifold method

2018 ◽  
Vol 21 (5) ◽  
pp. 824-837 ◽  
Author(s):  
Jian Huang ◽  
Gordon McTaggart-Cowan ◽  
Sandeep Munshi
Keyword(s):  
Natural Gas ◽  
Direct Injection ◽  
Moment Closure ◽  
Dimensional Manifold ◽  
Conditional Moment ◽  
Gas Combustion ◽  
Conditional Moment Closure ◽  
Natural Gas Combustion ◽  
Low Dimensional ◽  
Low Dimensional Manifold

This article describes the application of a modified first-order conditional moment closure model used in conjunction with the trajectory-generated low-dimensional manifold method in large-eddy simulation of pilot ignited high-pressure direct injection natural gas combustion in a heavy-duty diesel engine. The article starts with a review of the intrinsic low-dimensional manifold method for reducing detailed chemistry and various formulations for the construction of such manifolds. It is followed by a brief review of the conditional moment closure method for modelling the interaction between turbulence and combustion chemistry. The high computational cost associated with the direct implementation of the basic conditional moment closure model was discussed. The article then describes the formulation of a modified approach to solve the conditional moment closure equation, whose reaction source terms for the conditional mass fractions for species were obtained by projecting the turbulent perturbation onto the reaction manifold. The main model assumptions were explained and the resulting limitations were discussed. A numerical experiment was conducted to examine the validity the model assumptions. The model was then implemented in a combustion computational fluid dynamics solver developed on an open-source computational fluid dynamics platform. Non-reactive jet simulations were first conducted and the results were compared to the experimental measurement from a high-pressure visualization chamber to verify that the jet penetration under engine relevant conditions was correctly predicted. The model was then used to simulate natural gas combustion in a heavy-duty diesel engine equipped with a high-pressure direct injection system. The simulation results were compared with the experimental measurement from a research engine to verify the accuracy of the model for both the combustion rate and engine-out emissions.

W-LDMM: A Wasserstein driven low-dimensional manifold model for noisy image restoration

2020 ◽  
Vol 371 ◽  
pp. 108-123 ◽  
Author(s):  
Ruiqiang He ◽  
Xiangchu Feng ◽  
Weiwei Wang ◽  
Xiaolong Zhu ◽  
Chunyu Yang
Keyword(s):  
Image Restoration ◽  
Dimensional Manifold ◽  
Noisy Image ◽  
Low Dimensional ◽  
Low Dimensional Manifold
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