Low rank regularization: A review
Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …
approximately low rank assumption to target we aim to learn, which has achieved great …
Lq-lora: Low-rank plus quantized matrix decomposition for efficient language model finetuning
We propose a simple approach for memory-efficient adaptation of pretrained language
models. Our approach uses an iterative algorithm to decompose each pretrained matrix into …
models. Our approach uses an iterative algorithm to decompose each pretrained matrix into …
[HTML][HTML] Bridging convex and nonconvex optimization in robust PCA: Noise, outliers, and missing data
This paper delivers improved theoretical guarantees for the convex programming approach
in low-rank matrix estimation, in the presence of (1) random noise,(2) gross sparse outliers …
in low-rank matrix estimation, in the presence of (1) random noise,(2) gross sparse outliers …
Rapid robust principal component analysis: CUR accelerated inexact low rank estimation
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR …
In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR …
Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends
Multivariate calibration based on Partial Least Squares (PLS), Random Forest (RF) and
Support Vector Machine (SVM) methods combined with variable selections tools were used …
Support Vector Machine (SVM) methods combined with variable selections tools were used …
Speedup robust graph structure learning with low-rank information
Recent studies have shown that graph neural networks (GNNs) are vulnerable to
unnoticeable adversarial perturbations, which largely confines their deployment in many …
unnoticeable adversarial perturbations, which largely confines their deployment in many …
Online tensor robust principal component analysis
Online robust principal component analysis (RPCA) algorithms recursively decompose
incoming data into low-rank and sparse components. However, they operate on data vectors …
incoming data into low-rank and sparse components. However, they operate on data vectors …
Iteratively reweighted minimax-concave penalty minimization for accurate low-rank plus sparse matrix decomposition
Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer
vision and machine learning. It has been solved using convex relaxations of the matrix rank …
vision and machine learning. It has been solved using convex relaxations of the matrix rank …
Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms
This paper proposes an effective method for accurately recovering vessel structures and
intensity information from the X-ray coronary angiography (XCA) images of moving organs …
intensity information from the X-ray coronary angiography (XCA) images of moving organs …
Audio–Visual Segmentation based on robust principal component analysis
Abstract Audio–Visual Segmentation (AVS) aims to extract the sounding objects from a
video. The current learning-based AVS methods are often supervised, which rely on specific …
video. The current learning-based AVS methods are often supervised, which rely on specific …