Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior

Tichý, Ondřej; Bodiova, Lenka; Šmı́dl, Václav

doi:10.1109/lsp.2019.2897230

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…Two data loss scenarios are considered in this paper: random missing (RM) in which entries are missing randomly, and non‐random missing (NM) in which each PMU time series has block missing for randomly selected channels (e.g., 40% of the PMU channels have block missing lasting 40% of the time window T ). The algorithms for comparison include the proposed RFMR algorithms, CSI [25], MC based ADMM [31], low‐rank tensor completion (LRTC) [34], traditional truncated Hankel‐SVD [38], and Bayesian probabilistic matrix factorization (BPMF) [46]. Table 5 shows the results of the imputation task and the following fourfold conclusions can be derived: 1)When the PMU data are accurately observed, the completion performances of the proposed RFMR is not as good as the existing methods in some application scenarios. 2)The interpolation methods represented by CSI perform better recovery in random missing tasks, while BPMF has lower TVEs in non‐random missing recovery.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Two data loss scenarios are considered in this paper: random missing (RM) in which entries are missing randomly, and non-random missing (NM) in which each PMU time series has block missing for randomly selected channels (e.g., 40% of the PMU channels have block missing lasting 40% of the time window T ). The algorithms for comparison include the proposed RFMR algorithms, CSI [25], MC based ADMM [31], low-rank tensor completion (LRTC) [34], traditional truncated Hankel-SVD [38], and Bayesian probabilistic matrix factorization (BPMF) [46]. Table 5 shows the results of the imputation task and the following fourfold conclusions can be derived:…”

Section: Robust Recovery For Data Loss Under Low-snrsmentioning

confidence: 99%

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Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition

Pei

Wang

et al. 2022

IET Generation Trans & Dist

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The development of cyber-physical power systems raises concerns about the data quality issue of phasor measurement units (PMUs). Low signal-to-noise ratios (SNRs) and data losses caused by malicious electromagnetic interference, false data injections, and equipment malfunctioning may jeopardize the data integrity and availability necessary for power system monitoring, protection, and control. To ensure grid resiliency, this paper proposes a robust fast PMU measurement recovery (RFMR) algorithm based on improved singular spectrum analysis (SSA) of Hankel structures. It utilizes single or multiple channels of PMU time-series to restore the problematic phasor measurements with low-SNR noises and data losses. Additionally, the traditional singular value decomposition (SVD) and Tucker decomposition (TD) in RFMR are replaced by randomized SVD (RSVD) and sequential TD (STD) to reduce the computational complexity in single-channel and multi-channel RFMR, respectively. Numerical case studies demonstrate that the proposed algorithm can recover the noise-contaminated measurements with higher accuracy than existing methods, such as matrix/tensor decomposition approaches and robust principal component analysis (RPCA), and effectively complement the missing data with the observed measurements corrupted by low SNRs. Moreover, the latency margins of various power system synchrophasor application scenarios can be satisfied with the reduced computational complexity.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Robust Recovery For Data Loss Under Low-snrsmentioning

confidence: 99%

Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition

Pei

Wang

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In Algorithm 1, line 5 is solved classically since efficient algorithms exist for it [38], and would probably be applied with some kind of regularization since NMF and its variants tend to generally be ill-posed problems [17,39]. It is line 8 that is solved using BLLS (where QAOA would be applied).…”

Section: Non-negative Matrix Factorization (Nmf) and Variantsmentioning

confidence: 99%

Quantum approximate optimization for hard problems in linear algebra

Borle

Elfving²,

Lomonaco

2021

SciPost Phys. Core

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The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks. Here, we explore using QAOA for binary linear least squares (BLLS); a problem that can serve as a building block of several other hard problems in linear algebra, such as the non-negative binary matrix factorization (NBMF) and other variants of the non-negative matrix factorization (NMF) problem. Most of the previous efforts in quantum computing for solving these problems were done using the quantum annealing paradigm. For the scope of this work, our experiments were done on noiseless quantum simulators, a simulator including a device-realistic noise-model, and two IBM Q 5-qubit machines. We highlight the possibilities of using QAOA and QAOA-like variational algorithms for solving such problems, where trial solutions can be obtained directly as samples, rather than being amplitude-encoded in the quantum wavefunction. Our numerics show that even for a small number of steps, simulated annealing can outperform QAOA for BLLS at a QAOA depth of p\leq3p≤3 for the probability of sampling the ground state. Finally, we point out some of the challenges involved in current-day experimental implementations of this technique on cloud-based quantum computers.

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