Results 41 to 50 of about 13,997,719 (325)
Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition
IEEE Access, 2023 The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions. It is an approximation to the computationally prohibitive singular value decomposition (SVD). This work is concerned with a partial QLP decomposition of matrices
Maboud F. Kaloorazi +4 more
doaj +1 more source
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation [PDF]
IEEE transactions on intelligent transportation systems (Print), 2021 Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data.
Xinyu Chen +3 more
semanticscholar +1 more source
Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization
Remote Sensing, 2023 In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data.
Shicheng Yu +5 more
doaj +1 more source
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors [PDF]
Annual Meeting of the Association for Computational Linguistics, 2018 Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact ...
Zhun Liu +5 more
semanticscholar +1 more source
Low-Rank Few-Shot Adaptation of Vision-Language Models [PDF]
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Recent progress in the few-shot adaptation of VisionLanguage Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task.
Maxime Zanella, Ismail Ben Ayed
semanticscholar +1 more source
Seismic Data Denoising Based on Sparse and Low-Rank Regularization
Energies, 2020 Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li +4 more
doaj +1 more source
Low-rank Linear Fluid-structure Interaction Discretizations [PDF]
, 2020 Fluid-structure interaction models involve parameters that describe the solid and the fluid behavior. In simulations, there often is a need to vary these parameters to examine the behavior of a fluid-structure interaction model for different solids and ...
Benner, Peter +2 more
core +2 more sources
Separable and Low-Rank Continuous Games [PDF]
, 2007 In this paper, we study nonzero-sum separable games, which are continuous games whose payoffs take a sum-of-products form. Included in this subclass are all finite games and polynomial games. We investigate the structure of equilibria in separable games.
Asuman Ozdaglar +17 more
core +5 more sources
Fractional laplacians viscoelastic wave equation low-rank temporal extrapolation
Frontiers in Earth Science, 2023 The fractional Laplacians constant-Q (FLCQ) viscoelastic wave equation can describe seismic wave propagation accurately in attenuating media. A staggered-grid pseudo-spectral (SGPS) method is usually applied to solve this wave equation but it is of only ...
Hanming Chen +8 more
doaj +1 more source
Low-Rank Modeling and Its Applications in Image Analysis [PDF]
, 2014 Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and ...
Yang, Can +3 more
core +1 more source