The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the ...
Approximation of the stochastic Galerkin matrix in the low-rank canonical tensor format In this article we describe an efficient approximation of the stochastic Galerkin matrix which stems from a stationary diffusion equation. The uncertain pe... P W?Hnert,A Litvinenko,M Espig,... - 《Pamm》 被...
Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method. Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approxi... Eunwoo,Kim,Min...
Based on the observation that RSSs have high spatial correlation, we propose an efficient radio map construction method based on low-rank approximation. Different from the conventional interpolation methods, the proposed method represents the distribution of RSSs as a low-rank matrix and constructs ...
The algorithm uses an exact or approximative low-rank representation of the identity-by-descent matrix, which combined with the Woodbury formula for matrix inversion results in that the computations in the AI-REML iteration body can be performed more efficiently. For cases where an exact low-rank...
The algorithm uses an exact or approximative low-rank representation of the identity-by-descent matrix, which combined with the Woodbury formula for matrix inversion results in that the computations in the AI-REML iteration body can be performed more efficiently. For cases where an exact low-rank...
We examine the problem of planning a path through a low dimensional continuous state space subject to upper bounds on several additive cost metrics. For th... IB Mitchellt,S Sastry - IEEE Conference on Decision & Control 被引量: 104发表: 2004年 An Efficient Approximation Algorithm for Weighted...
@inproceedings{pela, author = {Yangyang Guo and Guangzhi Wang and Mohan Kankanhalli}, title = {PELA: Learning Parameter-Efficient Models with Low-Rank Approximation}, booktitle = {CVPR}, year = {2024} }About PELA: Learning Parameter-Efficient Models with Low-Rank Approximation [CVPR 2024] ...
cations of structured low-rank approximations are presented. Among the various algorithms, the singular value decomposi- tion (SVD) and nonnegative matrix factorization (NMF) [35] are widely used for exploratory data analysis. By embedding these classical low-rank algorithms into the proposed broad...
We present an efficient global optimization algorithm for exponential family principal component analysis (PCA) and associated low-rank matrix factorization problems. Exponential family PCA has been shown to improve the results of standard PCA on non-Gaussian data. Unfortunately, the widespread use of ...