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Low-Rank Tensor Methods for Model Order Reduction [chapter]

Anthony Nouy
2017 Handbook of Uncertainty Quantification  
Parametric or uncertainty analyses usually require the evaluation of an output of a model for many instances of the input parameters, which may be intractable for complex numerical models.  ...  This chapter provides an overview of low-rank methods for the approximation of functions that are identified either with order-two tensors (for vector-valued functions) or higher-order tensors (for multivariate  ...  They also include methods for the construction of reduced bases in S that exploit some prior information on u.  ... 
doi:10.1007/978-3-319-12385-1_21 fatcat:lvwdi5dbk5grhcopxffonj6tzy

Automatic recognition of facial expressions in image sequences: A review

R A Patil, Vineet Sahula, A. S. Mandal
2010 2010 5th International Conference on Industrial and Information Systems  
For human beings, facial expression is one of the most powerful and natural way to communicate their emotions and intensions.  ...  This paper reviews the past work done in solving these problems for image sequences. 978-1-4244-6653-5/10/$26.00  ...  A hierarchical optical flow method is used to track the optical flows of 13 × 13 windows surrounding the landmark points in the rest of the frames.  ... 
doi:10.1109/iciinfs.2010.5578670 fatcat:fjmsqip2p5etbnpp7ojx26sa7y

An Eigen-based motion retrieval method for real-time animation

Pengjie Wang, Rynson W.H. Lau, Zhigeng Pan, Jiang Wang, Haiyu Song
2014 Computers & graphics  
When applied to a motion database of 4 GB in size, our method requires approximately 20% of the standard time, making it more suitable for real-time animation.  ...  them less suitable for use with large motion databases.  ...  Acknowledgments We would like to thank all of the reviewers of this paper for their constructive comments.  ... 
doi:10.1016/j.cag.2013.11.008 fatcat:p3lj3pysi5fhvl4nwwaakosymu

Artistic Rendering of Portraits [chapter]

Mingtian Zhao, Song-Chun Zhu
2012 Computational Imaging and Vision  
The second factor is the artistic style, for example, sketch, painting, etc.  ...  These methods place the artist in the process, often during system training, in the hope that their talents may be tapped. Example based methods do not make this problem easy, however.  ...  Eigenspaces for shape and texture are computed from the sketch training set.  ... 
doi:10.1007/978-1-4471-4519-6_12 fatcat:3l6sym4hgbem5oqu2ehunv5zvi

A Block-Sparse Tensor Train Format for Sample-Efficient High-Dimensional Polynomial Regression

Michael Götte, Reinhold Schneider, Philipp Trunschke
2021 Frontiers in Applied Mathematics and Statistics  
Low-rank tensors are an established framework for the parametrization of multivariate polynomials.  ...  We propose to extend this framework by including the concept of block-sparsity to efficiently parametrize homogeneous, multivariate polynomials with low-rank tensors.  ...  The reason for this is that for the function dictionary of monomials Ψ monomial the eigenspaces of L for eigenvalue g are associated with homogeneous polynomials of degreee g.  ... 
doi:10.3389/fams.2021.702486 fatcat:dx7lluuxhbaehfwo55ti2jjioy

A Survey of Compressed GPU-Based Direct Volume Rendering [article]

Marcos Balsa Rodríguez, Enrico Gobbetti, José A. Iglesias Guitián, Maxim Makhinya, Fabio Marton, Renato Pajarola, Susanne K. Suter
2012 Eurographics State of the Art Reports  
Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time-varying or multi-volume visualization, or for networked visualization  ...  Great advancements in commodity graphics hardware have favored GPU-based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms.  ...  For example, we mention models, which are inherently hierarchical -even tough we are aware of the fact that many of the models can be constructed into a hierarchical data structure.  ... 
doi:10.2312/conf/eg2013/stars/117-136 fatcat:3cadb2miwngrjoaqmrmudww6lq

Edge Attention-based Multi-Relational Graph Convolutional Networks [article]

Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, Jinbo Bi
2018 arXiv   pre-print
By designing a dictionary for the edge attention, and forming the attention matrix of each molecule by looking up the dictionary, the EAGCN exploits correspondence between bonds in different molecules.  ...  The different attributes lead to different graph representations for the same molecule.  ...  Acknowledgments The authors would like to thank Dr. Minghu Song from the Center for Molecular Discovery at Yale University for discussions on model construction and chemical insights.  ... 
arXiv:1802.04944v2 fatcat:yxw37s6o2vcp5bvvljmyqhgyse

Kernel-based Inference of Functions over Graphs [article]

Vassilis N. Ioannidis, Meng Ma, Athanasios N. Nikolakopoulos, Georgios B. Giannakis, Daniel Romero
2018 arXiv   pre-print
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced.  ...  Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems.  ...  When the kernel matrices belong to the Laplacian family (16) , efficient algorithms that exploit the common eigenspace of the kernels in the dictionary have been developed in [61] .  ... 
arXiv:1711.10353v2 fatcat:4qcuuc7vznhopdc6owi6pj7xzu

Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis [article]

William K. Allard, Guangliang Chen, Mauro Maggioni
2011 arXiv   pre-print
Their construction is fast, and so are the algorithms that map data points to dictionary coefficients and vice versa.  ...  In this paper we construct data-dependent multi-scale dictionaries that aim at efficient encoding and manipulating of the data.  ...  First, method (I) leads to the sparsest coefficients for each point, while method (II) produces the smallest dictionary.  ... 
arXiv:1105.4924v3 fatcat:fklitqoq5bfklnshnneaafpka4

2020 Index IEEE Transactions on Signal Processing Vol. 68

2020 IEEE Transactions on Signal Processing  
., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises; TSP  ...  ., +, TSP 2020 343-358 Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty.  ...  ., +, TSP 2020 4824-4838 Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty.  ... 
doi:10.1109/tsp.2021.3055469 fatcat:6uswtuxm5ba6zahdwh5atxhcsy

Table of Contents

2020 IEEE Transactions on Signal Processing  
Jadbabaie 4178 Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Routtenberg 1152 A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsp.2020.3042287 fatcat:nh7viihaozhd7li3txtadnx5ui

Table of Contents [EDICS]

2020 IEEE Transactions on Signal Processing  
Khalid 4568 A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Zhang 2077 Eigenspace Solution for AOA Localization in Modified Polar Representation . . . . . . . . . . . . Y. Sun, K. C. Ho, and Q.  ...  Optimization Methods for Signal Processing Support Recovery for Sparse Signals With Unknown Non-Stationary Modulation . . . . . Y. Xie, M. B. Wakin, and G.  ... 
doi:10.1109/tsp.2020.3045363 fatcat:wcnvdcy3rvhblh7rtxfe6gz4re

Probabilistic object and viewpoint models for active object recognition

Natasha Govender, Jonathan Warrell, Philip Torr, Fred Nicolls
2013 2013 Africon  
For mobile robots to perform certain tasks in human environments, fast and accurate object verification and recognition is essential.  ...  An early method to adopt a Bayesian approach [5] used an appearance based object representation, namely a parametric eigenspace distribution, and updated the object and pose hypotheses using Bayes' theorem  ...  This is the same representation used in [12] . The vocabulary tree is constructed using hierarchical kmeans clustering where similar features are clustered together.  ... 
doi:10.1109/afrcon.2013.6757598 fatcat:k7aooosbbnhgfevrrbdcznywwa

Face Recognition Techniques: A Survey

V.Vi jayakumari
2013 World Journal of Computer Application and Technology  
These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.  ...  Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition.  ...  The method was extended to the detection of features under different viewing geometries by using either a view-based Eigen space or a parametric eigenspace.  ... 
doi:10.13189/wjcat.2013.010204 fatcat:zv5jrbcmazexnigyzim5smajpa

Low-rank methods for high-dimensional approximation and model order reduction [article]

Anthony Nouy
2015 arXiv   pre-print
Tensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated.  ...  uncertainty quantification or parametric analyses.  ...  Algorithms have also been proposed for an adaptive construction of low-rank approximations of a in Tensor Train format [72] or Hierarchical Tucker format [4] .  ... 
arXiv:1511.01554v1 fatcat:rie2wsdlebf2vmskjkpk275wry
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