CUR {ccTensor} | R Documentation |
The input data is assumed to be a matrix. CUR decomposes the matrix to three low-dimensional factor matices. C and R are not estimated values but the actual column and row vectors sampled from the matrix.
CUR(A, c.rank=NULL, r.rank=NULL, thr=0.9, c.method=c("best.match", "random", "exact.num.random", "top.scores"), u.method=c("invCR", "invW"), r.method=c("best.match", "random", "exact.num.random", "top.scores"))
A |
The input matrix which has N-rows and M-columns. |
c.rank |
The number of low-dimension of C (J1 < N,M). If this argument is not specified or specified as NULL, the low-dimension is estimated based on the cumulative singular value (Default: NULL). |
r.rank |
The number of low-dimension of R (J2 < N,M). If this argument is not specified or specified as NULL, the low-dimension is estimated based on the cumulative singular value (Default: NULL). |
thr |
The threshold to determine the low-dimension J1 and J2. The value must be range 0 to 1 (Default: 0.9). |
c.method |
The column sampling algorithm (Default: best.match). |
u.method |
The algorithm to calculate U (Default: invCR). |
r.method |
The row sampling algorithm (Default: best.match). |
C: A N-rows and J1-columns matrix contains the sampled column vectors from the input matrix A. U: A J1-rows and J2-columns matrix. R: A J2-rows and M-columns matrix contains the sampled row vectors from the input matrix A. indC: The sampled column indices. indC: The sampled row indices. RecError : The reconstruction error between data matrix and reconstructed matrix from C and X.
Koki Tsuyuzaki
Michael W. Mahoney, et. al., (2009). CUR matrix decompositions for improved data analysis. PNAS
library("ccTensor") library("nnTensor") # Test data matdata <- toyModel(model = "NMF") # Simple usage out <- CUR(matdata, c.rank=3, r.rank=4)