computeM: Perform ADMM
In mkarikom/RSoptSC: Low-Rank Factorization for Subspace-clustering
computeM R Documentation
Perform ADMM
Description
Compute cell-to-cell similarity matrix by solving the following
optimization problem via ADMM
min_{Z,E} ||Z||_* + lambda ||E||_{2,1}\\
s.t. X = XZ + E\\
Z'1 = 1\\
Z_{i,j} = 0 for (i,j) \in Omega
Usage
computeM(D, X, lambda)
Arguments
D
the unweighted KNN adjacency matrix
X
normalized sample vectors
lambda
the balance term between the rank of Z and the error
Value
a list containing the low rank approximation of X and manifold learning error
mkarikom/RSoptSC documentation built on May 10, 2023, 1:10 a.m.
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| computeM | R Documentation |
Perform ADMM
Description
Compute cell-to-cell similarity matrix by solving the following optimization problem via ADMM
min_{Z,E} ||Z||_* + lambda ||E||_{2,1}\\
s.t. X = XZ + E\\
Z'1 = 1\\
Z_{i,j} = 0 for (i,j) \in Omega
Usage
computeM(D, X, lambda)
Arguments
D |
the unweighted KNN adjacency matrix |
X |
normalized sample vectors |
lambda |
the balance term between the rank of Z and the error |
Value
a list containing the low rank approximation of X and manifold learning error
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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