jointNMF: Joint Decomposition with Nonnegative Matrix Factorization

In CHuanSite/PJD: Partial and Joint Decomposiiton(PJD)

Description

Joint decomposition of several linked matrices with Nonnegative Matrix Factorization (NMF) It is based on the MSE loss, proposed by Lee, Daniel D., and H. Sebastian Seung. "Learning the parts of objects by non-negative matrix factorization." Nature 401.6755 (1999): 788-791.

Usage

jointNMF(
  dataset,
  group,
  comp_num,
  weighting = NULL,
  max_ite = 100,
  max_err = 1e-04,
  proj_dataset = NULL,
  proj_group = NULL
)

Arguments

dataset	A list of dataset to be analyzed
group	A list of grouping of the datasets, indicating the relationship between datasets
comp_num	A vector indicates the dimension of each compoent
weighting	Weighting of each dataset, initialized to be NULL
max_ite	The maximum number of iterations for the jointNMF algorithms to run, default value is set to 100
max_err	The maximum error of loss between two iterations, or the program will terminate and return, default value is set to be 0.0001
proj_dataset	The datasets to be projected on
proj_group	The grouping of projected data sets

Value

A list contains the component and the score of each dataset on every component after jointNMF algorithm

Examples

dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
group = list(c(1,2,3,4), c(1,2), c(3,4), c(1,3), c(2,4), c(1), c(2), c(3), c(4))
comp_num = c(2,2,2,2,2,2,2,2,2)
proj_dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
proj_group = list(c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE))
res_jointNMF = jointNMF(dataset, group, comp_num, proj_dataset = proj_dataset, proj_group = proj_group)

CHuanSite/PJD documentation built on Oct. 26, 2021, 1 p.m.