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Journal articles on the topic 'Correlation Matrix'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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1

Sukhanova, E. M. "Matrix Correlation." Theory of Probability & Its Applications 54, no. 2 (January 2010): 347–55. http://dx.doi.org/10.1137/s0040585x97984231.

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2

Qian, Weixian, Xiaojun Zhou, Yingcheng Lu, and Jiang Xu. "Paths correlation matrix." Optics Letters 40, no. 18 (September 14, 2015): 4336. http://dx.doi.org/10.1364/ol.40.004336.

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3

Sonneveld, P., J. J. I. M. van Kan, X. Huang, and C. W. Oosterlee. "Nonnegative matrix factorization of a correlation matrix." Linear Algebra and its Applications 431, no. 3-4 (July 2009): 334–49. http://dx.doi.org/10.1016/j.laa.2009.01.004.

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4

Harnad, J., and M. Jacques. "The supersymmetric soliton correlation matrix." Journal of Geometry and Physics 2, no. 1 (January 1985): 1–15. http://dx.doi.org/10.1016/0393-0440(85)90015-4.

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5

Neudecker, H., and A. M. Wesselman. "The asymptotic variance matrix of the sample correlation matrix." Linear Algebra and its Applications 127 (1990): 589–99. http://dx.doi.org/10.1016/0024-3795(90)90363-h.

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6

Meng, Jong, and Chun Xia Guo. "A Optimization Model of Correlation Matrix." Advanced Materials Research 482-484 (February 2012): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.270.

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Hamilton and Pelletier have brought forward the regime switching model for dynamic correlation matrix. It is especially for the variance time-varying financial multiple time series. In this paper, we use random matrix theory to improve the correlation matrix, which removed the noise, leaving the true information. We also present an empirical application use China's stock market data to optimize portfolio which illustrates that our model can have achieved good results.
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7

Foucart, Thierry. "Numerical Analysis of a Correlation Matrix." Statistics 29, no. 4 (January 1997): 347–61. http://dx.doi.org/10.1080/02331889708802595.

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8

Ti, Guan, Geng Yujie, Ma Qiang, Liu Yong, and Lin Lin. "Communication Network Discovery through Correlation Matrix." Journal of Physics: Conference Series 1345 (November 2019): 022070. http://dx.doi.org/10.1088/1742-6596/1345/2/022070.

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9

Burda, Z., A. Görlich, A. Jarosz, and J. Jurkiewicz. "Signal and noise in correlation matrix." Physica A: Statistical Mechanics and its Applications 343 (November 2004): 295–310. http://dx.doi.org/10.1016/j.physa.2004.05.048.

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10

Frappat, L., A. Sciarrino, and P. Sorba. "Correlation matrix for quartet codon usage." Physica A: Statistical Mechanics and its Applications 351, no. 2-4 (June 2005): 461–76. http://dx.doi.org/10.1016/j.physa.2005.01.051.

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11

Bergère, M. C. "Correlation functions of complex matrix models." Journal of Physics A: Mathematical and General 39, no. 28 (June 27, 2006): 8749–73. http://dx.doi.org/10.1088/0305-4470/39/28/s01.

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12

Hao, Cheng, Guo Wei, and Yu Jingdong. "Subspace decomposition-based correlation matrix multiplication." Journal of Systems Engineering and Electronics 19, no. 2 (April 2008): 241–45. http://dx.doi.org/10.1016/s1004-4132(08)60073-0.

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13

Levin, I. A., D. Otero, A. N. Proto, and V. Zunino. "Correlation-operators in density matrix formulation." Physica A: Statistical Mechanics and its Applications 151, no. 2-3 (August 1988): 447–56. http://dx.doi.org/10.1016/0378-4371(88)90026-x.

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14

Kang, Jian, Johan Stax Jakobsen, Annastiina Silvennoinen, Timo Teräsvirta, and Glen Wade. "A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model." Econometrics 10, no. 3 (August 24, 2022): 30. http://dx.doi.org/10.3390/econometrics10030030.

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We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given.
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15

Dass, Peter Michael, Joseph Jauro Deshi, Fartisincha Peingurta Andrew, and Buba Mamman Wufem. "Phytochemical screening, quantification and correlation matrix of Nigerian medicinal plant: Waltheria americana." AROC in Natural Products Research 1, no. 2 (September 9, 2021): 9–16. http://dx.doi.org/10.53858/arocnpr01020916.

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Background: Plant’s kingdom provides new and important leads against various pharmacological targets due to the current wide spread of belief that green medicine is safe and more dependable than the costly synthetic drugs. The medicinal property of plants step from their ability to synthesize aromatic substances and secondary metabolites that are potent bioactive compounds found in medicinal plant parts that are precursors for the synthesis of useful drugs. In the present study, the leaf, stem, and root extracts of Waltheria americana were evaluated for phytochemical compositions and their correlation matrix. Methods: Quantitative and quantitative standard methods of analysis were used to evaluate the presence, amount, and the correlationships of the different phytochemicals in the leaf, root and stem of W. americana plant. Results: The quantitative phytochemicals percentage composition of W. americana varied with large ranges for alkaloids, tannins, flavonoids, but short ranges occurred of terpenes and cardiac glycosides. Alkaloids had the highest percentage composition and cardiac glycosides showed the lowest for all the plant parts. The stem seems to be the major area of phytochemical production than other parts of the plant, indicating that the stem of W. americana could serve as a major source of phytochemicals in any herbal concoction. “The correlation” of phytochemical constituents, alkaloids and tannins in the leaf were positively and significantly correlated with cardiac glycosides in the stem at 95% confidence respectiely. However, no correlation was observed of any phytochemicals in the other plant. Conclusion: These findings indicated that the production, quantification, and distribution of these phytochemicals were complimentary in nature in Waltheria americana plant, and the shoot may have played a major role in this regard
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16

Jiang, Wei, Guodong Qin, and Jian Dong. "DOA Estimation for a Passive Synthetic Array Based on Cross-Correlation Matrix." International Journal of Signal Processing Systems 5, no. 2 (June 2017): 55–59. http://dx.doi.org/10.18178/ijsps.5.2.55-59.

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17

Rehman, Mutti-Ur, Jehad Alzabut, and Kamaleldin Abodayeh. "Computing Nearest Correlation Matrix via Low-Rank ODE’s Based Technique." Symmetry 12, no. 11 (November 4, 2020): 1824. http://dx.doi.org/10.3390/sym12111824.

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For n-dimensional real-valued matrix A, the computation of nearest correlation matrix; that is, a symmetric, positive semi-definite, unit diagonal and off-diagonal entries between −1 and 1 is a problem that arises in the finance industry where the correlations exist between the stocks. The proposed methodology presented in this article computes the admissible perturbation matrix and a perturbation level to shift the negative spectrum of perturbed matrix to become non-negative or strictly positive. The solution to optimization problems constructs a gradient system of ordinary differential equations that turn over the desired perturbation matrix. Numerical testing provides enough evidence for the shifting of the negative spectrum and the computation of nearest correlation matrix.
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18

Grote, Hartmut. "Mind-Matter Entanglement Correlations: Blind Analysis of a new Correlation Matrix Experiment." Journal of Scientific Exploration 35, no. 2 (June 15, 2021): 287–310. http://dx.doi.org/10.31275/20211931.

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The work reported here is a rigorous conceptual replication of the so-called “Correlation-Matrix” experiment by an independent author. The experiment has been built from scratch with new hardware and software, testing 200 participants that have spent about half an hour each trying to ‘influence’ a physical random process visualized for feedback. The analysis software has been conceptualized following a strict blind analysis protocol. Blind analysis is a more rigid form of pre-registered analysis, in which the complete analysis software is written and tested before the data is actually analysed for the effect under study. The unblinding of the analysis, also called ‘opening of the box’ of the experiment described here has been performed live at the PA convention 2019 in Paris. The main result was found to be not statistically significant and fell well within the expected random distribution of possible results. A second experiment, also following a blind analysis protocol, included questionnaires that were correlated with the participants’ performance to ‘influence’ the physical random process (the main psi task). This yielded a probability of p=0.06 to have occurred by chance, under a null hypothesis. A post-hoc analysis of the hit rate for the psi task across all participants, which is mathematically independent from the correlation analysis, yielded a probability of p=0.06 as well, to have occurred by chance. Three unexpected anecdotal incidences that occurred during the execution of the experiment and the testing and actual analysis of the data may add to the canon of oddities and trickster-like effects sometimes reported in parapsychology research.
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19

Kurowicka, Dorota. "Joint density of correlations in the correlation matrix with chordal sparsity patterns." Journal of Multivariate Analysis 129 (August 2014): 160–70. http://dx.doi.org/10.1016/j.jmva.2014.04.006.

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20

Mestre, Xavier, and Pascal Vallet. "Correlation Tests and Linear Spectral Statistics of the Sample Correlation Matrix." IEEE Transactions on Information Theory 63, no. 7 (July 2017): 4585–618. http://dx.doi.org/10.1109/tit.2017.2689780.

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21

Zusmanovich, Pasha. "On near and the nearest correlation matrix." Journal of Nonlinear Mathematical Physics 20, no. 3 (July 3, 2013): 431–39. http://dx.doi.org/10.1080/14029251.2013.855050.

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22

Yue, Wenzhen, Yan Zhang, and Jingwen Xie. "QPSK signal design for given correlation matrix." Electronics Letters 52, no. 5 (March 2016): 399–401. http://dx.doi.org/10.1049/el.2015.3963.

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23

Jiang, Tiefeng. "Determinant of sample correlation matrix with application." Annals of Applied Probability 29, no. 3 (June 2019): 1356–97. http://dx.doi.org/10.1214/17-aap1362.

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24

Tawfik, Bassel. "The correlation memory matrix for parameter estimation." Applied Mathematics and Computation 111, no. 1 (May 2000): 87–101. http://dx.doi.org/10.1016/s0096-3003(99)00105-8.

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25

Ishiki, Goro, and Chaiho Rim. "Boundary correlation numbers in one matrix model." Physics Letters B 694, no. 3 (November 2010): 272–77. http://dx.doi.org/10.1016/j.physletb.2010.10.001.

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26

Turner, Mick, and Jim Austin. "Matching performance of binary correlation matrix memories." Neural Networks 10, no. 9 (December 1997): 1637–48. http://dx.doi.org/10.1016/s0893-6080(97)00059-2.

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27

Rea, Alethea, and William Rea. "Visualization of a stock market correlation matrix." Physica A: Statistical Mechanics and its Applications 400 (April 2014): 109–23. http://dx.doi.org/10.1016/j.physa.2014.01.017.

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28

Nagao, Taro. "Pfaffian Expressions for Random Matrix Correlation Functions." Journal of Statistical Physics 129, no. 5-6 (September 22, 2007): 1137–58. http://dx.doi.org/10.1007/s10955-007-9415-9.

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29

Falissard, Bruno. "A spherical representation of a correlation matrix." Journal of Classification 13, no. 2 (September 1996): 267–80. http://dx.doi.org/10.1007/bf01246102.

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30

Goldreich, Yair. "Distcorrelation Matrix: A Spatial Correlation Trend Analysis." Geographical Analysis 16, no. 4 (September 3, 2010): 358–68. http://dx.doi.org/10.1111/j.1538-4632.1984.tb00821.x.

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31

Smouse, Peter E., and Jeffrey C. Long. "Matrix correlation analysis in anthropology and genetics." American Journal of Physical Anthropology 35, S15 (1992): 187–213. http://dx.doi.org/10.1002/ajpa.1330350608.

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32

Elliott, Peter, and Neepa T. Maitra. "Density-matrix propagation driven by semiclassical correlation." International Journal of Quantum Chemistry 116, no. 10 (February 1, 2016): 772–83. http://dx.doi.org/10.1002/qua.25087.

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33

Martellosio, Federico. "THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS." Econometric Theory 28, no. 6 (April 27, 2012): 1373–91. http://dx.doi.org/10.1017/s0266466612000175.

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This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties of W to correlation properties of SAR(1) models defined on irregular lattices.
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34

Gao, Wenyun, Sheng Dai, Stanley Ebhohimhen Abhadiomhen, Wei He, and Xinghui Yin. "Low Rank Correlation Representation and Clustering." Scientific Programming 2021 (February 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/6639582.

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Correlation learning is a technique utilized to find a common representation in cross-domain and multiview datasets. However, most existing methods are not robust enough to handle noisy data. As such, the common representation matrix learned could be influenced easily by noisy samples inherent in different instances of the data. In this paper, we propose a novel correlation learning method based on a low-rank representation, which learns a common representation between two instances of data in a latent subspace. Specifically, we begin by learning a low-rank representation matrix and an orthogonal rotation matrix to handle the noisy samples in one instance of the data so that a second instance of the data can linearly reconstruct the low-rank representation. Our method then finds a similarity matrix that approximates the common low-rank representation matrix much better such that a rank constraint on the Laplacian matrix would reveal the clustering structure explicitly without any spectral postprocessing. Extensive experimental results on ORL, Yale, Coil-20, Caltech 101-20, and UCI digits datasets demonstrate that our method has superior performance than other state-of-the-art compared methods in six evaluation metrics.
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35

Nel, D. G. "A matrix derivation of the asymptotic covariance matrix of sample correlation coefficients." Linear Algebra and its Applications 67 (June 1985): 137–45. http://dx.doi.org/10.1016/0024-3795(85)90191-0.

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36

Zhou, Changhe, and Liren Liu. "Binary-encoded matrix-matrix multiplication architecture based on correlation by shadow casting." Microwave and Optical Technology Letters 7, no. 9 (June 20, 1994): 421–23. http://dx.doi.org/10.1002/mop.4650070913.

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37

Prószyński, Witold, and Mieczysław Kwaśniak. "An attempt to determine the effect of increase of observation correlations on detectability and identifiability of a single gross error." Geodesy and Cartography 65, no. 2 (December 1, 2016): 313–33. http://dx.doi.org/10.1515/geocart-2016-0018.

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Abstract The paper presents the results of investigating the effect of increase of observation correlations on detectability and identifiability of a single gross error, the outlier test sensitivity and also the response-based measures of internal reliability of networks. To reduce in a research a practically incomputable number of possible test options when considering all the non-diagonal elements of the correlation matrix as variables, its simplest representation was used being a matrix with all non-diagonal elements of equal values, termed uniform correlation. By raising the common correlation value incrementally, a sequence of matrix configurations could be obtained corresponding to the increasing level of observation correlations. For each of the measures characterizing the above mentioned features of network reliability the effect is presented in a diagram form as a function of the increasing level of observation correlations. The influence of observation correlations on sensitivity of the w-test for correlated observations (Förstner 1983, Teunissen 2006) is investigated in comparison with the original Baarda’s w-test designated for uncorrelated observations, to determine the character of expected sensitivity degradation of the latter when used for correlated observations. The correlation effects obtained for different reliability measures exhibit mutual consistency in a satisfactory extent. As a by-product of the analyses, a simple formula valid for any arbitrary correlation matrix is proposed for transforming the Baarda’s w-test statistics into the w-test statistics for correlated observations.
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38

Collis, Betty A., and Lorne K. Rosenblood. "The Problem of Inflated Significance When Testing Individual Correlations from a Correlation Matrix." Journal for Research in Mathematics Education 16, no. 1 (January 1985): 52. http://dx.doi.org/10.2307/748973.

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39

Strahov, Eugene. "Dynamical correlation functions for products of random matrices." Random Matrices: Theory and Applications 04, no. 04 (October 2015): 1550020. http://dx.doi.org/10.1142/s2010326315500203.

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We introduce and study a family of random processes with a discrete time related to products of random matrices. Such processes are formed by singular values of random matrix products, and the number of factors in a random matrix product plays a role of a discrete time. We consider in detail the case when the (squared) singular values of the initial random matrix form a polynomial ensemble, and the initial random matrix is multiplied by standard complex Gaussian matrices. In this case, we show that the random process is a discrete-time determinantal point process. For three special cases (the case when the initial random matrix is a standard complex Gaussian matrix, the case when it is a truncated unitary matrix, or the case when it is a standard complex Gaussian matrix with a source) we compute the dynamical correlation functions explicitly, and find the hard edge scaling limits of the correlation kernels. The proofs rely on the Eynard–Mehta theorem, and on contour integral representations for the correlation kernels suitable for an asymptotic analysis.
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40

Whitehill, Sam. "An introduction to pricing correlation products using a pair-wise correlation matrix." Journal of Credit Risk 5, no. 1 (March 2009): 97–110. http://dx.doi.org/10.21314/jcr.2009.083.

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41

Brincker, Rune, Sandro D. R. Amador, Martin Juul, and Manuel Lopez-Aenelle. "Modal Participation Estimated from the Response Correlation Matrix." Shock and Vibration 2019 (August 28, 2019): 1–10. http://dx.doi.org/10.1155/2019/9347075.

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In this paper, we are considering the case of estimating the modal participation vectors from the operating response of a structure. Normally, this is done using a fitting technique either in the time domain using the correlation function matrix or in the frequency domain using the spectral density matrix. In this paper, a more simple approach is proposed based on estimating the modal participation from the correlation matrix of the operating responses. For the case of general damping, it is shown how the response correlation matrix is formed by the mode shape matrix and two transformation matrices T1 and T1 that contain information about the modal parameters, the generalized modal masses, and the input load spectral density matrix Gx. For the case of real mode shapes, it is shown how the response correlation matrix can be given a simple analytical form where the corresponding real modal participation vectors can be obtained in a simple way. Finally, it is shown how the real version of the modal participation vectors can be used to synthesize empirical spectral density functions.
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42

González, Juan Martín Casillas, and Antonio Alatorre Torres. "Random Matrix Approach to Correlation Matrix of Financial Data (Mexican Stock Market Case)." Modern Economy 06, no. 09 (2015): 1033–42. http://dx.doi.org/10.4236/me.2015.69099.

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43

ARIMA, AKITO. "CORRELATION BETWEEN EIGENVALUES AND SORTED DIAGONAL MATRIX ELEMENTS OF A LARGE DIMENSIONAL MATRIX." International Journal of Modern Physics E 17, supp01 (December 2008): 334–41. http://dx.doi.org/10.1142/s0218301308011963.

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Functional dependences of eigenvalues as functions of sorted diagonal elements are given for realistic nuclear shell model (NSM) hamiltonian, the uniform distribution hamiltonian and the GOE hamiltonian. In the NSM case, the dependence is found to be linear. We discuss extrapolation methods for more accurate predictions for low-lying states.
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44

Liu, C., J. Liu, Y. X. Yao, P. Wu, C. Z. Wang, and K. M. Ho. "Correlation Matrix Renormalization Theory: Improving Accuracy with Two-Electron Density-Matrix Sum Rules." Journal of Chemical Theory and Computation 12, no. 10 (September 6, 2016): 4806–11. http://dx.doi.org/10.1021/acs.jctc.6b00570.

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45

Zhang, Xiaoxia, Degang Chen, and Kesheng Wu. "Incremental nonnegative matrix factorization based on correlation and graph regularization for matrix completion." International Journal of Machine Learning and Cybernetics 10, no. 6 (March 15, 2018): 1259–68. http://dx.doi.org/10.1007/s13042-018-0808-7.

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46

Fushiki, Tadayoshi. "Estimation of Positive Semidefinite Correlation Matrices by Using Convex Quadratic Semidefinite Programming." Neural Computation 21, no. 7 (July 2009): 2028–48. http://dx.doi.org/10.1162/neco.2009.04-08-765.

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The correlation matrix is a fundamental statistic that used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive purposes. Since the correlation is a natural similarity measure between users, the correlation matrix may be used as the Gram matrix in kernel methods. However, the estimated correlation matrix sometimes has a serious defect: although the correlation matrix is originally positive semidefinite, the estimated one may not be positive semidefinite when not all ratings are observed. To obtain a positive semidefinite correlation matrix, the nearest correlation matrix problem has recently been studied in the fields of numerical analysis and optimization. However, statistical properties are not explicitly used in such studies. To obtain a positive semidefinite correlation matrix, we assume an approximate model. By using the model, an estimate is obtained as the optimal point of an optimization problem formulated with information on the variances of the estimated correlation coefficients. The problem is solved by a convex quadratic semidefinite program. A penalized likelihood approach is also examined. The MovieLens data set is used to test our approach.
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47

ZHANG, Hong, Fei WU, and Xiao-Long ZHANG. "Multimedia Data Clustering Based on Correlation Matrix Fusion." Chinese Journal of Computers 34, no. 9 (October 15, 2011): 1705–11. http://dx.doi.org/10.3724/sp.j.1016.2011.01705.

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48

Yastrebov, A. V. "Iterative inversion algorithm for an interference correlation matrix." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 2 (June 30, 2017): 4–9. http://dx.doi.org/10.38013/2542-0542-2017-2-4-9.

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The article considers efficiency of a multi-channel interference canceller as a function of the number of compensation channels used for a fixed number of jammers. The author suggests an iterative inversion algorithm for an interference correlation matrix, making it possible to achieve zero loss at a certain stage and decrease the computational complexity of determining weight factors for compensation channels
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49

He, Jie, Da-zheng Feng, Hui Lü, and Cong Xiang. "Two-dimensional Adaptive Beamforming Based on Correlation Matrix." Journal of Electronics & Information Technology 32, no. 12 (January 19, 2011): 2890–94. http://dx.doi.org/10.3724/sp.j.1146.2009.01500.

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50

Pham-Gia, Thu, and Vartan Choulakian. "Distribution of the Sample Correlation Matrix and Applications." Open Journal of Statistics 04, no. 05 (2014): 330–44. http://dx.doi.org/10.4236/ojs.2014.45033.

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