Matrix factorization techniques in machine learning, signal processing, and statistics
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
Cross tensor approximation methods for compression and dimensionality reduction
Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …
Learned robust PCA: A scalable deep unfolding approach for high-dimensional outlier detection
Robust principal component analysis (RPCA) is a critical tool in modern machine learning,
which detects outliers in the task of low-rank matrix reconstruction. In this paper, we propose …
which detects outliers in the task of low-rank matrix reconstruction. In this paper, we propose …
Robust CUR decomposition: Theory and imaging applications
This paper considers the use of robust principal component analysis (RPCA) in a CUR
decomposition framework and applications thereof. Our main algorithms produce a robust …
decomposition framework and applications thereof. Our main algorithms produce a robust …
Tensor CUR decomposition under T-product and its perturbation
J Chen, Y Wei, Y Xu - Numerical Functional Analysis and …, 2022 - Taylor & Francis
In order to process the large-scale data, a useful tool in dimensionality reduction of a matrix,
the CUR decomposition has been developed, which can compress the huge matrix with its …
the CUR decomposition has been developed, which can compress the huge matrix with its …
Matrix completion with cross-concentrated sampling: Bridging uniform sampling and CUR sampling
While uniform sampling has been widely studied in the matrix completion literature, CUR
sampling approximates a low-rank matrix via row and column samples. Unfortunately, both …
sampling approximates a low-rank matrix via row and column samples. Unfortunately, both …
Kernel matrix approximation on class-imbalanced data with an application to scientific simulation
P Hajibabaee, F Pourkamali-Anaraki… - IEEE …, 2021 - ieeexplore.ieee.org
Generating low-rank approximations of kernel matrices that arise in nonlinear machine
learning techniques holds the potential to significantly alleviate the memory and …
learning techniques holds the potential to significantly alleviate the memory and …
Fast robust tensor principal component analysis via fiber CUR decomposition
We study the problem of tensor robust principal component analysis (TRPCA), that aims to
separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their …
separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their …
Fast cross tensor approximation for image and video completion
This paper presents a framework that suggests the utilization of cross tensor approximation
or tensor CUR approximation to reconstruct incomplete images and videos. The proposed …
or tensor CUR approximation to reconstruct incomplete images and videos. The proposed …
Tensor Robust CUR for Compression and Denoising of Hyperspectral Data
MM Salut, DV Anderson - IEEE Access, 2023 - ieeexplore.ieee.org
Hyperspectral images are often contaminated with noise which degrades the quality of data.
Recently, tensor robust principal component analysis (TRPCA) has been utilized to remove …
Recently, tensor robust principal component analysis (TRPCA) has been utilized to remove …