Results 31 to 40 of about 256,120 (256)
Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion
Remote Sensing, 2022 Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis.
Wei Zhang +5 more
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Improved sparse low-rank matrix estimation [PDF]
Signal Processing, 2017 10 pages, 10 ...
Ankit Parekh, Ivan W. Selesnick
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An Insight to Estimated Item Response Matrix in Item Response Theory
IEEE Access, 2023 This paper investigates the performance of item response theory based on distance criteria rather than likelihood criteria. For this purpose, the estimated item response matrix is introduced.
Hideo Hirose
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Maximum Entropy Low-Rank Matrix Recovery [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2018 Fixing ...
Simon Mak, Yao Xie 0002
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Low-Rank Matrix Completion: A Contemporary Survey
IEEE Access, 2019 As a paradigm to recover unknown entries of a matrix from partial observations, low-rank matrix completion (LRMC) has generated a great deal of interest.
Luong Trung Nguyen +2 more
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Low-rank density-matrix evolution for noisy quantum circuits
npj Quantum Information, 2021 In this work, we present an efficient rank-compression approach for the classical simulation of Kraus decoherence channels in noisy quantum circuits. The approximation is achieved through iterative compression of the density matrix based on its leading ...
Yi-Ting Chen +2 more
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Low-Rank Matrix Factorization Method for Multiscale Simulations: A Review
IEEE Open Journal of Antennas and Propagation, 2021 In this paper, a review of the low-rank factorization method is presented, with emphasis on their application to multiscale problems. Low-rank matrix factorization methods exploit the rankdeficient nature of coupling impedance matrix blocks between two ...
Mengmeng Li +5 more
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Matrix recovery with implicitly low-rank data [PDF]
Neurocomputing, 2019 In this paper, we study the problem of matrix recovery, which aims to restore a target matrix of authentic samples from grossly corrupted observations. Most of the existing methods, such as the well-known Robust Principal Component Analysis (RPCA), assume that the target matrix we wish to recover is low-rank.
Xingyu Xie +3 more
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Low-Rank Matrix Completion via QR-Based Retraction on Manifolds
Mathematics, 2023 Low-rank matrix completion aims to recover an unknown matrix from a subset of observed entries. In this paper, we solve the problem via optimization of the matrix manifold. Specially, we apply QR factorization to retraction during optimization. We devise
Ke Wang +3 more
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Low-rank Matrix Recovery With Unknown Correspondence
CoRR, 2021 We study a matrix recovery problem with unknown correspondence: given the observation matrix $M_o=[A,\tilde P B]$, where $\tilde P$ is an unknown permutation matrix, we aim to recover the underlying matrix $M=[A,B]$. Such problem commonly arises in many applications where heterogeneous data are utilized and the correspondence among them are unknown, e ...
Zhiwei Tang +3 more
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