Low-rank matrix restoration, which aims to recover low-rank structures from degraded observation matrices, has been extensively studied in computer vision. However, the existing methods always suffer from data information loss caused by over shrinkage of the rank component or heavy computation burden ...
Weighted bilinear factorization of low-rank matrix with structural smoothness for image denoising Collectively, above rationally introduced ingredients enables our algorithm to enhance the structural smoothness and effectively remove large sparse noise in the case of natural images with low rank attributes. ...
uncertainty · Bilinear approach · Low-rank matrix approximation · Least squares estimate · Mahalanobis distance · Conic fitting 1 Introduction Parameter estimation can be reduced as solving a linear ma- trix equation. However, the total least squares (TLS) based ...
Adaptive quantile low-rank matrix factorization distributions, significant efforts have been made on optimizing the (weighted) L1 or L2-norm loss between an observed matrix and its bilinear factorization... S Xu,C Zhang,J Zhang - 《Pattern Recognition》 被引量: 0发表: 2020年 An Adaptive Bileve...
structured robust bilinear factorization (RBF) method to recover low-rank and sparse matrices from missing and grossly corrupted data, i.e., robust matrix completion (RMC), or incomplete and grossly corrupted measurements, i.e., compressive principal component pursuit (CPCP). Specifically, we ...
Bilinear low rank matrix factorization (BLRMF)Bi-nuclear quasi-normADMM2019 Elsevier B.V. Hyperspectral images (HSIs) have rich spectral information, but the various noises generated during the imaging process destroy the visual quality of images and lower the application precision. Therefore, it's...
Bilinear factorizationSample factoringCosine similarity metricLow-rank hyperspectral image recovery (LRHSIR) is a very challenging task in various computer vision applications for its inherent complexity. Hyperspectral image (HSI) contains much more information than a regular image due to significant number...
An Efficient Bilinear Factorization based Method For Motion Capture Data RefinementAs a preprocessing step, motion capture (mocap) data refinement is to predict missing data and remove noises and outliers. In recent years, low-rank matrix completion has been successfully applied to mocap dara ...
> n the matrix X T X will be rank deficient. In this situation dimensionality reduction or regulariza- tion is often necessary. A common approach is to independently learn low-dimensional 1 Bold capital letters denote matrices X, bold lower-case letters a column vector x. xj represents ...
Bilinear factorizationLow-rank representationMulti-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are ...