Results 311 to 320 of about 3,813,510 (369)

Orthogonal Subspace Projection Using Data Sphering and Low-Rank and Sparse Matrix Decomposition for Hyperspectral Target Detection

IEEE Transactions on Geoscience and Remote Sensing, 2021
Orthogonal subspace projection (OSP) has been widely used in many applications for hyperspectral data exploitation. However, its performance is sensitive to its used prior target knowledge, which is significantly affected by target background (BKG).
Chein-I. Chang, Jie Chen
semanticscholar   +1 more source

Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
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 and l0-pseudo-norm, which are the nuclear norm and l1-norm, respectively ...
P. Pokala, Raghu vamshi Hemadri, C. Seelamantula   +2 more
semanticscholar   +1 more source

Fast Numerical Computation of Two-Dimensional Non-Separable Linear Canonical Transform Based on Matrix Decomposition

IEEE Transactions on Signal Processing, 2021
Two-dimensional non-separable linear canonical transform (2D NSLCT), as a generalized form of linear canonical transform (LCT) and 2D separable linear canonical transform (2D SLCT), has important applications in many engineering fields.
Deyun Wei, Yi Shen
semanticscholar   +1 more source

Small Matrix Decomposition of Feynman Path Amplitudes.

Journal of Chemical Theory and Computation, 2021
The small matrix decomposition of the path integral (SMatPI) is employed to devise expressions for the quantum mechanical amplitude of forward-backward paths in the path integral formulation.
N. Makri
semanticscholar   +1 more source

Hyperspectral Images Denoising via Nonconvex Regularized Low-Rank and Sparse Matrix Decomposition

IEEE Transactions on Image Processing, 2020
Hyperspectral images (HSIs) are often degraded by a mixture of various types of noise during the imaging process, including Gaussian noise, impulse noise, and stripes. Such complex noise could plague the subsequent HSIs processing.
Ting Xie, Shutao Li, Bin Sun
semanticscholar   +1 more source

A Truncated Matrix Decomposition for Hyperspectral Image Super-Resolution

IEEE Transactions on Image Processing, 2020
Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI).
Jianjun Liu, Zebin Wu, Liang Xiao, Jun Sun, Hong Yan   +4 more
semanticscholar   +1 more source

Compressing Pre-trained Language Models by Matrix Decomposition

AACL, 2020
Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI).
M. Noach, Yoav Goldberg
semanticscholar   +1 more source

Decomposition of a symmetric matrix

Numerische Mathematik, 1976
An algorithm is presented to compute a triangular factorization and the inertia of a symmetric matrix. The algorithm is stable even when the matrix is not positive definite and is as fast as Cholesky. Programs for solving associated systems of linear equations are included.
Linda Kaufman, James R. Bunch, Beresford N. Parlett   +2 more
openaire   +2 more sources

A Low-Rank and Sparse Matrix Decomposition- Based Dictionary Reconstruction and Anomaly Extraction Framework for Hyperspectral Anomaly Detection

IEEE Geoscience and Remote Sensing Letters, 2020
As one of the important applications of hyperspectral imagery (HSI) processing, the Mahalanobis distance-based detector in anomaly detection used to extract knowledge from the background and then calculate the Mahalanobis distance to obtain the detection
Yichu Xu, Bo Du, Liangpei Zhang, Shizhen Chang   +3 more
semanticscholar   +1 more source

Application of Matrix Decompositions for Matrix Canonization

Computational Mathematics and Mathematical Physics, 2019
© 2019, Pleiades Publishing, Ltd. Abstract: The problem of solving overdetermined, underdetermined, singular, or ill conditioned SLAEs using matrix canonization is considered. A modification of an existing canonization algorithm based on matrix decomposition is proposed.
Volkov V., Dem’yanov D.
openaire   +3 more sources