CX {ccTensor}

CX Matrix Decomposition

Description

The input data is assumed to be a matrix. CX decomposes the matrix to two low-dimensional factor matices. C is not an estimated values but the actual column vectors sampled from the matrix.

Usage

CX(A, rank=NULL, thr=0.9,
  c.method=c("best.match", "random", "exact.num.random", "top.scores"))

Arguments

A

The input matrix which has N-rows and M-columns.

rank

The number of low-dimension (J < 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 J. The value must be range 0 to 1 (Default: 0.9).

c.method

The column sampling algorithm (Default: best.match).

Value

C: A N-rows and J-columns matrix contains the sampled column vectors from the input matrix A. X: A J-rows and M-columns matrix. indC: The sampled column indices. RecError : The reconstruction error between data matrix and reconstructed matrix from C and X.

Author(s)

Koki Tsuyuzaki

References

Petros Drineas et.al., (2008). Relative-error CUR Matrix Decompositions. SIAM J. Matrix Anal. Appl.

Examples

    library("ccTensor")
    library("nnTensor")
    # Test data
    matdata <- toyModel(model = "NMF")
    # Simple usage
    out <- CX(matdata, rank=5)