Results 11 to 20 of about 14,259,947 (309)
IET Signal Processing, 2022 Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete ...
Eysan Mehrbani, Mohammad Hossein Kahaei
doaj +3 more sources
Low Rank Regularization: A review [PDF]
Neural Networks, 2021 Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications.
Zhanxuan Hu +3 more
openaire +3 more sources
Low-rank Parareal: a low-rank parallel-in-time integrator
BIT Numerical Mathematics, 2023 AbstractIn this work, the Parareal algorithm is applied to evolution problems that admit good low-rank approximations and for which the dynamical low-rank approximation (DLRA) can be used as time stepper. Many discrete integrators for DLRA have recently been proposed, based on splitting the projected vector field or by applying projected Runge–Kutta ...
Carrel, Benjamin +2 more
openaire +5 more sources
CoRR, 2021 We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of well-known methods for it, including vector auto-regressive models, state-space methods, multi-task regression, and ...
Shane T. Barratt +2 more
openaire +2 more sources
Low rank phase retrieval [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 To appear in IEEE Trans.
Seyedehsara Nayer +2 more
openaire +2 more sources
Beyond low rank + sparse: Multi-scale low rank matrix decomposition [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales.
Frank Ong, Michael Lustig
openaire +3 more sources
IEEE Open Journal of Control Systems, 2023 Several recent empirical studies demonstrate that important machine learning tasks such as training deep neural networks, exhibit a low-rank structure, where most of the variation in the loss function occurs only in a few directions of the input space ...
Romain Cosson +4 more
doaj +1 more source
A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint
Remote Sensing, 2021 Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing.
Xiaobin Wu +4 more
doaj +1 more source
International Journal of Coal Science & Technology, 2017 Benzene carboxylic acid (BCAs) are common and useful chemical blocks, which can be derived from the abundant low rank coals (LRCs) via oxidative degradation. In this work, we proposed a novel strategy to utilize BCAs as raw materials to prepare catalysts
Huacong Zhou +8 more
doaj +1 more source
Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices [PDF]
arXiv.org, 2023 In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs). In contrast to LoRA and other low-rank adaptation methods such as AdaLoRA, Delta-LoRA not only updates the low-rank matrices ...
Bojia Zi +5 more
semanticscholar +1 more source