eigengap.oht: Rank selection via optimal hard thresholding
In sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent
eigengap.oht R Documentation
Rank selection via optimal hard thresholding
Description
Select the number of significant principal components of a matrix via optimal
hard thresholding (OHT)
Usage
eigengap.oht(covmat, nobs, maxcomp = NULL)
Arguments
covmat
matrix to be decomposed
nobs
number of observations used to compute the covariance matrix
maxcomp
maximum number of eigenvalues to compute
References
Gavish, M., Donoho, D.L. (2014)
The optimal hard thresholding for singular values is 4/sqrt(3).
IEEE Transactions on Information Theory, 60(8): 5040–5053
sgdGMF documentation built on April 3, 2025, 7:37 p.m.
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| eigengap.oht | R Documentation |
Rank selection via optimal hard thresholding
Description
Select the number of significant principal components of a matrix via optimal hard thresholding (OHT)
Usage
eigengap.oht(covmat, nobs, maxcomp = NULL)
Arguments
covmat |
matrix to be decomposed |
nobs |
number of observations used to compute the covariance matrix |
maxcomp |
maximum number of eigenvalues to compute |
References
Gavish, M., Donoho, D.L. (2014) The optimal hard thresholding for singular values is 4/sqrt(3). IEEE Transactions on Information Theory, 60(8): 5040–5053
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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