A new weakly supervised discrete discriminant hashing for robust data representation

Wan, Minghua; Chen, Xueyu; Zhao, Cairong; Zhan, Tianming; Yang, Guowei

doi:10.1016/j.ins.2022.08.015

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Data security is essential to financial control, especially in the context of growing cyber security threats. Data encryption technologies protect financial and non-financial information during storage and transfer (Chen and Wang 2023) while hashing is used to verify the integrity of data without the need to reveal its contents (Wan et al 2022). Caching improves the performance of financial control systems by providing fast access to frequently used information.…”

Section: Digital Tools For Information Managementmentioning

confidence: 99%

Investigating the Application of Digital Tools for Information Management in Financial Control: Evidence from Bulgaria

Zhelev,

Kostova

2024

JRFM

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This paper discusses the application of digital information management tools in the context of financial control. In Bulgaria, such research is innovative as it is the first time that digital transformation in crucial financial control institutions, which influence the formation of the revenue part of the state budget and the spending of public funds, has been studied. The study aims to answer the research question of to what extent the application of digital tools in financial control improves its effectiveness. It analyses how modern technologies improve the efficiency and accuracy of information used in financial control institutions. The authors examine the impact of digital tools, such as database management systems, business analytics platforms, and electronic document management tools, on collecting, analyzing, and managing financial and non-financial information. The study uses descriptive statistics and a correlation analysis, which significantly contributes to establishing the relationship between implemented digital tools and improvements in financial control procedures. The results show that despite the conditions created for digitalization in financial control institutions, digital tools are used to a limited extent in the information management process. The study emphasizes the need for continuous investment in digital technologies and training to maximize the benefits of their application in financial control.

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Section: Digital Tools For Information Managementmentioning

confidence: 99%

Investigating the Application of Digital Tools for Information Management in Financial Control: Evidence from Bulgaria

Zhelev,

Kostova

2024

JRFM

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“…In recent years, l 1 -norm [10] has been greatly developed, and when the image is noisy, the recognition accuracy of the image is still high [11][12][13][14][15]. To further improve the robustness of subspace learning method, l p -norm is proposed, and because of it, PCA [16] and LDA [17] are further developed.…”

Section: Introductionmentioning

confidence: 99%

“…Equation (29) shows that the objective function of Equation ( 12) decreases monotonically in each iteration. Combining the convergence conditions given by Algorithm 1, it can be determined that the objective function (12) has a lower bound, and finally converges to the local optimal solution, so Theorem 2 is true.…”

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confidence: 98%

Manifold Regularized Principal Component Analysis Method Using L2,p-Norm

et al. 2022

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The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l2,p-norm PCA and manifold learning method. To improve robustness, the model method adopts l2,p-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p=0.5. Finally, the experimental results show that the method proposed in this paper is robust and effective.

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