Zobrazeno 1 - 10
of 1 627
pro vyhledávání: '"Data matrix (multivariate statistics)"'
Publikováno v:
European Journal of Operational Research, 297(2), 782-794. Elsevier
The linear regression model remains an important workhorse for data scientists. However, many data sets contain many more predictors than observations. Besides, outliers, or anomalies, frequently occur. This paper proposes an algorithm for regression
Publikováno v:
CCF Transactions on High Performance Computing. 3:252-270
Kernel ridge regression (KRR) is a fundamental method in machine learning. Given an n-by-d data matrix as input, a traditional implementation requires $$\Theta (n^2)$$ memory to form an n-by-n kernel matrix and $$\Theta (n^3)$$ flops to compute the f
Two-Dimensional Localization: Low-Rank Matrix Completion With Random Sampling in Massive MIMO System
Publikováno v:
IEEE Systems Journal. 15:3628-3631
In this paper, random sampling is considered for direction-of-arrival (DOA) estimation with reduced hardware complexity in massive multiple-input–multiple-output (MIMO) systems. The resulting problem is that the accuracy of the existing DOA estimat
Autor:
Atikur R. Khan, Enamul Kabir
Publikováno v:
Journal of Data, Information and Management. 3:225-235
Sharing microdata within or outside of an organization may lead to the disclosure of sensitive information of an individual. Data stewarding organizations often disseminate synthetic data to reduce the likelihood of disclosure of sensitive informatio
Publikováno v:
International Journal of Data Science and Analytics. 13:47-61
Unsupervised outlier detection without the need for clean data has attracted great attention because it is suitable for real-world problems as a result of its low data collection costs. Reconstruction-based methods are popular approaches for unsuperv
Publikováno v:
Neurocomputing. 440:127-144
Low-rank approximation of matrices plays an important role in many application scenarios, including image denoising. This paper introduces a new low-rank approximation method named minimum unbiased risk estimate formulation of 2DPCA (MURE-2DPCA). MUR
Autor:
Terrence D. Jorgensen
Publikováno v:
Psych
Volume 3
Issue 2
Pages 11-133
Psych, 3(2), 113-133. MDPI
Psych, Vol 3, Iss 11, Pp 113-133 (2021)
Volume 3
Issue 2
Pages 11-133
Psych, 3(2), 113-133. MDPI
Psych, Vol 3, Iss 11, Pp 113-133 (2021)
Structural equation modeling (SEM) has been proposed to estimate generalizability theory (GT) variance components, primarily focusing on estimating relative error to calculate generalizability coefficients. Proposals for estimating absolute-error com
Assessing alternatives to the development of administrativeeconomic units applying the FARE-M Method
Publikováno v:
ADMINISTRATIE SI MANAGEMENT PUBLIC. :6-26
The socio-economic development of economic-territorial units subordinate to administrative-management institutions appears as one of the main tasks. The values of alternative indicators reflecting socio-economic development may differ, which makes it
Publikováno v:
Mathematical Models and Computer Simulations. 13:382-394
We develop/propose the method reducing the dimension of a data matrix, based on its direct and inverse projection, and the calculation of projectors that minimize the cross-entropy functional, remove. We introduce the concept of information capacity
Publikováno v:
Maisog, J M, Demarco, A T, Devarajan, K, Young, S, Fogel, P & Luta, G 2021, ' Assessing methods for evaluating the number of components in non-negative matrix factorization ', Mathematics, vol. 9, no. 22, 2840 . https://doi.org/10.3390/math9222840
Mathematics, Vol 9, Iss 2840, p 2840 (2021)
Mathematics
Volume 9
Issue 22
Mathematics, Vol 9, Iss 2840, p 2840 (2021)
Mathematics
Volume 9
Issue 22
Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m × n data matrix X into an m × k matrix W and a k × n matrix H, so that X ≈ W × H. Importantly, all values in X, W, and H are constrained to