Results 11 to 20 of about 615,565 (289)
pyGNMF: A Python library for implementation of generalised non-negative matrix factorisation method
SoftwareX, 2022 This article introduces a Python library named pyGNMF, which implements the recently proposed generalised non-negative matrix factorisation (GNMF) method.
Nirav L. Lekinwala, Mani Bhushan
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
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The effect on cosmological parameter estimation of a parameter dependent covariance matrix
The Open Journal of Astrophysics, 2019 Cosmological large-scale structure analyses based on two-point correlation functions often assume a Gaussian likelihood function with a fixed covariance matrix. We study the impact on cosmological parameter estimation of ignoring the parameter dependence
Darsh Kodwani +2 more
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Sparse estimation of a covariance matrix [PDF]
Biometrika, 2011 We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even ...
Jacob Bien, Robert J. Tibshirani
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A cautionary note on robust covariance plug-in methods [PDF]
, 2014 Many multivariate statistical methods rely heavily on the sample covariance matrix. It is well known though that the sample covariance matrix is highly non-robust.
Nordhausen, Klaus, Tyler, David E.
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The Intraclass Covariance Matrix [PDF]
Behavior Genetics, 2005 Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives.
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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation [PDF]
Climate of the Past, 2019 Data assimilation has been adapted in paleoclimatology to reconstruct past climate states. A key component of some assimilation systems is the background-error covariance matrix, which controls how the information from observations spreads into the model
V. Valler +5 more
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Remarks on covariant matrix strings [PDF]
Physics Letters B, 1999 15 pages, harvmac.
Baulieu, Laurent +2 more
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A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition
Sensors, 2012 This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting ...
Yantao Li +3 more
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Discriminant methods for high dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is singular.
Poompong Kaewumpai, Samruam Chongcharoen
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