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Application of principal components analysis results in visual network analysis
Biosciences Biotechnology Research Asia. 2015. Vol. 12. P. 609–617.
Denisenko A., Krylov G. O.
We show the way principal components analysis could be used to preprocess the dataset for the purposes of visualization of a particular property of the object. PCA was used to synthesize scores which were visualized in network analysis process. This provides analyst with a holistic picture of the presense of a particular property in the selection of analyzed objects.
Kachan O., Yanovich Y., Abramov E., Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki 2018 Vol. 160 No. 2 P. 300–308
According to the manifold hypothesis, high-dimensional data can be viewed and meaning- fully represented as a lower-dimensional manifold embedded in a higher dimensional feature space. Manifold learning is a part of machine learning where an intrinsic data representation is uncovered based on the manifold hypothesis.
Many manifold learning algorithms were developed. The one called Grassmann&Stiefel eigenmaps ...
Added: January 21, 2026
Chikake T. M., Goldengorin B. I., Pardalos P. M., Computer Optics 2025 Vol. 49 No. 6 P. 1191–1201
We present a general-purpose, training-free framework for dimensionality reduction and clustering based on per–sample pseudo–Boolean polynomials (PBP). The method constructs compact, interpreTab. features without model fitting and is evaluated under a standardized protocol that compares PBP to PCA, t-SNE, and UMAP using identical inputs and metrics: clustering alignment (V-measure, Adjusted Rand Index), cluster geometry (Silhouette coefficient, ...
Added: January 2, 2026
Минасян К. Х., Ponamorenko V., Российский юридический журнал 2025 № 2 С. 62–76
This article is devoted to a comparative analysis of the legal, institutional, methodological, and informational-technical frameworks for countering the laundering of corruption proceeds in the Russian Federation and the Republic of Armenia.
The authors aim, through this analysis, to formulate recommendations for improving mechanisms to combat the laundering of corruption proceeds in Russia and the Eurasian ...
Added: May 12, 2025
IEEE, 2020.
Dimensionality reduction problem is stated as finding a mapping f:X ∈ R m → Z ∈ R n , where ≪ m while preserving some relevant properties of the data. We formulate topology-preserving dimensionality reduction as finding the optimal orthogonal projection to the lower-dimensional subspace which minimizes discrepancy between persistent diagrams of the original data and the projection. This ...
Added: October 14, 2021
Kosov M., Аудиторские ведомости 2020 № 4 С. 26–29
There is room for improvement in the area of state financial control in the Russian Federation. The article highlights the factors that determine the development of state financial control in Russia, including the incompleteness of legal regulation of this sphere. In view of this, the improvement of legal regulation has been identified as areas for ...
Added: September 2, 2021
Popkov Y., Popkov A., Dubnov Y. A., Информатика и ее применения 2020 Т. 14 № 4 С. 47–54
The work is devoted to development of methods for deterministic and randomized projection aimed at dimensionality reduction problems. In the deterministic case, the authors develop the parallel reduction procedure minimizing Kullback-Leibler cross-entropy target to condition on information capacity based on the gradient projection method. In the randomized case, the authors solve the problem of reduction ...
Added: January 26, 2021
Minabutdinov A., Bouev M., Manaev I., Journal of Computational Finance 2020 Vol. 24 No. 2 P. 103–127
We consider the problem of finding a valid covariance matrix in the foreign
exchange market given an initial non-PSD estimate of such a matrix. The common
no-arbitrage assumption imposes additional linear constraints on such matrices,
whereby inevitably making them singular. As a result, even the most advanced
numerical techniques will predictably balk at a seemingly standard optimization
task. The reason ...
Added: October 31, 2020
Popkov Y., Popkov A., Dubnov Y. A., Математическое моделирование 2020 Т. 32 № 9 С. 35–52
We develop a new method of dimensionality reduction based on direct and inverse projection of data matrix and calculation of projectors minimizing cross-entropy functional. Concept of information capacity of matrix which is used as a restriction in a problem of optimal reduction is introduced. We conduct a comparison of proposed method with known ones based ...
Added: October 31, 2020
Калинина А. А., Pochinok A., Экономика. Налоги. Право 2014 № 3 С. 12–16
The paper analyzes the impact of interstate tax competition on the movement of financial flows in the world and identifies the main negative effects of tax competition. In particular, it reveals that the high level of tax burden leads to the shadow economy and encourages taxpayers to move their business in more favorable tax jurisdictions. ...
Added: November 13, 2019
Dubnov Y. A., Информационные технологии и вычислительные системы 2018 № 2 С. 60–69
The paper considers the problem of reducing the dimension of the feature space for describing objects
in data analysis problems using the example of binary classification. The article provides a detailed
overview of existing approaches to solving this problem and proposes several modifications. In which
the dimensionality reduction is considered as the problem of extracting the most relevant ...
Added: July 4, 2018
Haufe S., Dähne S., Nikulin V., Neuroimage 2014 No. 101 P. 583–597
Neuronal oscillations have been shown to be associated with perceptual, motor and cognitive brain operations. While complex spatio-temporal dynamics are a hallmark of neuronal oscillations, they also represent a formidable challenge for the proper extraction and quantification of oscillatory activity with non-invasive recording techniques such as EEG and MEG. In order to facilitate the study ...
Added: October 23, 2014
Kuleshov A. P., Bernstein A., , in: Machine Learning and Data Mining in Pattern RecognitionVol. 8556.: Springer, 2014. P. 119–133.
Many Data Mining tasks deal with data which are presented in high dimensional spaces, and the ‘curse of dimensionality’ phenomena is often an obstacle to the use of many methods for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used as a first key step in solutions of these tasks to ...
Added: September 30, 2014
Berlin, Heidelberg: Springer, 2011.
This volume contains the extended version of selected talks given at the international research workshop "Coping with Complexity: Model Reduction and Data Analysis", Ambleside, UK, August 31 – September 4, 2009. The book is deliberately broad in scope and aims at promoting new ideas and methodological perspectives. The topics of the chapters range from theoretical ...
Added: May 29, 2013
Агалаков Ю. Г., Bernstein A., Информационные технологии и вычислительные системы 2012 № 3 С. 3–17
Рассматриваются задачи интеллектуального анализа данных, которые необходимо решать в технологии предсказательного моделирования. Для уменьшения сложности решения этих задач в технологии предсказательного моделирования используются решения задач снижения размерности, которые должны удовлетворять ряду дополнительных условий. В статье обсуждаются эти дополнительные требования и сформулированы соответствующие новые нетрадиционные постановки задач снижения размерности. ...
Added: January 24, 2013