Bayesian Matrix Factorization for Semibounded Data

Dalhoumi, Oumayma; Bouguila, Nizar; Amayri, Manar; Fan, Wentao

doi:10.1109/tnnls.2021.3111824

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Bayesian Network (BN) is often used in safety assessment. A combination of graph theory and probability theory, BN was proposed by Pearl in 1986 [22][23][24][25]. BN can effectively express and describe uncertain knowledge, making it suitable for safety assessment.…”

Section: Introductionmentioning

confidence: 99%

Safety Assessment of Transport Aircraft Heavy Equipment Airdrop: An Improved STPA-BN Mechanism

Tian

Kan

et al. 2022

IEEE Access

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Aviation safety has been an eternal theme, and safety assessment is taken as the important method to assess the aircraft safety, but it is difficult to assess the safety of a heavy equipment airdrop from a transport aircraft due to mission complexity and strong coupling. This study uses an improved STPA-BN methodology to assess the safety of transport aircraft heavy equipment airdrop. To this purpose, qualitative safety analysis is performed by STPA, and then quantitative safety assessment is performed by BN. In contrast to the-state-of-the-art, the distinguishing feature of the proposed design lies in the introduction of DS evidence theory, which makes the probability of nodes in BN a priori. More precisely, best-worst multicriteria decision-making method (BWM) is skillfully incorporated into the DS evidence theory so as to tackle the conflict issue that exists in most existing designs. Importance analysis is utilized to determine the importance of the different risk nodes. A case of transport aircraft heavy equipment airdrop mission is used to test the effectiveness of the proposed method.INDEX TERMS Heavy equipment airdrop, STPA-BN, BWM, Safety assessment.

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Section: Introductionmentioning

confidence: 99%

Safety Assessment of Transport Aircraft Heavy Equipment Airdrop: An Improved STPA-BN Mechanism

Tian

Kan

et al. 2022

IEEE Access

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“…NMF has been extensively studied in recent years [9,10,11,12,13,14,15], which has found applications in various areas, such as pattern recognition [16], multimedia analysis [17], and text mining [18]. Basically, the goal of the original NMF is to approximate a nonnegative matrix with two nonnegative factor matrices with physical meanings [8].…”

Section: Introductionmentioning

confidence: 99%

Log-based Sparse Nonnegative Matrix Factorization for Data Representation

Peng¹,

Zhang²,

Chen³

et al. 2022

Preprint

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Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness. Moreover, we propose a novel column-wisely sparse norm, named 2,log -(pseudo) norm to enhance the robustness of the proposed method. The 2,log -(pseudo) norm is invariant, continuous, and differentiable. For the 2,log regularized shrinkage problem, we derive a closed-form solution, which can be used for other general problems. Efficient multiplicative updating rules are developed for the optimization, which theoretically guarantees the convergence of the objective value sequence. Extensive experimental results confirm the effectiveness of the proposed method, as well as the enhanced sparseness and robustness.

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