Results 21 to 30 of about 14,259,947 (309)
Low‐rank magnetic resonance fingerprinting [PDF]
Medical Physics, 2016 PurposeMagnetic resonance fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed offline, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch
Mazor, Gal +3 more
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Low-Rank Sinkhorn Factorization
CoRR, 2021 Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector products are pervasive in the Sinkhorn algorithm, several works have proposed to \textit{approximate} kernel matrices appearing in its iterations using low-rank factors ...
Meyer Scetbon +2 more
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Non-Negative Low Rank Graph Embedding Algorithm
Jisuanji kexue yu tansuo, 2020 The existing non-negative matrix factorization (NMF) algorithms still have some shortcomings. On one hand, the NMF method calculates its low-dimensional representation directly on the high-dimensional original image data set, but in fact the effective ...
LIU Guoqing, LU Guifu, ZHOU Sheng, XUAN Dongdong, CAO Along
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InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning [PDF]
Computer Vision and Pattern RecognitionContinual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity ...
Yan-Shuo Liang, Wu-Jun Li
semanticscholar +1 more source
Generalized Low Rank Models [PDF]
Foundations and Trends® in Machine Learning, 2016 Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.
Madeleine Udell +3 more
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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
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The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing [PDF]
International Conference on Machine Learning, 2023 We propose $\textsf{ScaledGD($\lambda$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned.
Xingyu Xu +3 more
semanticscholar +1 more source
A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising
Remote Sensing, 2020 Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods.
Shaobo Li +4 more
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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
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