| Title: | Hierarchical Non-Negative Matrix Factorization |
| Version: | 1.0 |
| Author: | Nicolas Sauwen |
| Maintainer: | Nicolas Sauwen <nicolas.sauwen@openanalytics.eu> |
| Description: | Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available. |
| Depends: | R (≥ 3.3.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | NMF, oro.nifti, tcltk, nnls, rasterImage, stats, graphics, grDevices, MASS |
| RoxygenNote: | 6.0.1.9000 |
| Suggests: | testthat |
| NeedsCompilation: | no |
| Packaged: | 2020-11-20 12:54:35 UTC; nsauwen |
| Repository: | CRAN |
| Date/Publication: | 2020-11-20 13:30:02 UTC |
Accelerated hierarchical alternating least squares NMF. For a reference to the method, see N. Gillis, Nonnegative matrix factorization: complexity, algorithms and applications [Section 4.2, Algo. 6], PhD thesis, Université catholique de Louvain, February 2011.
Description
Accelerated hierarchical alternating least squares NMF. For a reference to the method, see N. Gillis, Nonnegative matrix factorization: complexity, algorithms and applications [Section 4.2, Algo. 6], PhD thesis, Université catholique de Louvain, February 2011.
Usage
HALSacc(X, nmfMod, alpha = 1, maxiter = 1000, checkDivergence = FALSE)
Arguments
X |
Input data matrix, each column represents one observation and the rows correspond to the different features |
nmfMod |
Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition) |
alpha |
Nonnegative parameter of the accelerated method |
maxiter |
Maximum number of iterations |
checkDivergence |
currently not in use, to be implemented |
Value
Resulting NMF model (in accordance with the NMF package definition)
Author(s)
nsauwen
NMF by alternating non-negative least squares using projected gradients. For a reference to the method, see C.-J. Lin, "Projected Gradient Methods for Non-negative Matrix Factorization", Neural computation 19.10 (2007): 2756-2779.
Description
NMF by alternating non-negative least squares using projected gradients. For a reference to the method, see C.-J. Lin, "Projected Gradient Methods for Non-negative Matrix Factorization", Neural computation 19.10 (2007): 2756-2779.
Usage
PGNMF(X, nmfMod, tol = 1e-05, maxIter = 500, timeLimit = 300,
checkDivergence = TRUE)
Arguments
X |
Input data matrix, each column represents one data point and the rows correspond to the different features |
nmfMod |
Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition) |
tol |
Tolerance for a relative stopping condition |
maxIter |
Maximum number of iterations |
timeLimit |
Limit of time duration NMF analysis |
checkDivergence |
Boolean indicating whether divergence checking should be performed Default is TRUE, but it should be set to FALSE when using random initialization |
Value
Resulting NMF model (in accordance with the NMF package definition)
Author(s)
nsauwen
Hierarchical non-negative matrix factorization.
Description
Hierarchical non-negative matrix factorization.
Usage
hNMF(nmfInput, nmfMethod = "HALSacc")
Arguments
nmfInput |
List with NMF input attributes |
nmfMethod |
String referring to the NMF algorithm to be used. |
Value
Resulting NMF model (in accordance with NMF package definition)
Author(s)
Nicolas Sauwen
Examples
# create nmfInput object
X <- matrix(runif(10*20), 10,20)
bgImageTensor <- array(0,dim=dim(X))
selectVect <- array(1,dim=dim(X))
nmfInput <- NULL
nmfInput$numRows <- nrow(X)
nmfInput$numCols <- ncol(X)
nmfInput$numSlices <- 1
nmfInput$bgImageTensor <- bgImageTensor
nmfInput$selectVect <- selectVect
# run NMF with default algorithm, 5 runs with random initialization
NMFresult1 <- oneLevelNMF(X, rank=2, nruns=5)
# run NMF with specified algorithm and with initialized sources
W0 <- initializeSPA(X,3)
NMFresult2 <- oneLevelNMF(X, rank=3, method="HALSacc", initData = W0)
Overlay a mask or a color scaled image on top of a background image
Description
Overlay a mask or a color scaled image on top of a background image
Usage
imoverlay(image, overlay, selectVect = NULL, color = c(0, 1, 0))
Arguments
image |
A matrix, background image |
overlay |
A matrix, serving as the overlay mask or figure |
selectVect |
A matrix (binary values), specifying which matrix elements are to be overlaid |
color |
3-element vector, defining the RGB color to be used in case the overlay is a mask |
Author(s)
Nicolas Sauwen
Initialize NMF model with initial spectral data
Description
Initialize NMF model with initial spectral data
Usage
initializeNMF(X, initData = NULL)
Arguments
X |
input matrix |
initData |
source or abundance matrix with initial values |
The successive projection algorithm, a useful method for initializing the NMF source matrix
Description
The successive projection algorithm, a useful method for initializing the NMF source matrix
Usage
initializeSPA(data, nSources)
Arguments
data |
Input data matrix. The columns correspond to the data points, each row represents one feature |
nSources |
Number of sources to be obtained |
Value
Matrix with initialized sources as its columns
Author(s)
Nicolas Sauwen
Examples
# random data
X <- matrix(runif(10*20), 10,20)
# Create initial source matrix for 3 sources
W0 <- initializeSPA(X,3)
Perform Non-Negative Matrix factorization
Description
Perform Non-Negative Matrix factorization
Usage
oneLevelNMF(X, rank, initData = NULL, method = "PGNMF", nruns = 10,
checkDivergence = TRUE)
Arguments
X |
input matrix. Each column represents one observation and the rows correspond to the different features |
rank |
number of NMF components to be found |
initData |
either of the NMF factor matrices, with initial values |
method |
name of the NMF method to be used. "PGNMF" (default) and "HALSacc" are available by default. Any method from the NMF package can also be specified |
nruns |
number of NMF runs. It is recommended to run the NMF analyses multiple times when random seeding is used, to avoid a suboptimal solution |
checkDivergence |
Boolean indicating whether divergence checking should be performed |
Value
Scaled NMF model (in accordance with the NMF package definition)
Author(s)
Nicolas Sauwen
Examples
# random data
X <- matrix(runif(10*20), 10,20)
# run NMF with default algorithm, 5 runs with random initialization
NMFresult1 <- oneLevelNMF(X, rank=2, nruns=5)
# run NMF with specified algorithm and with initialized sources
W0 <- initializeSPA(X,3)
NMFresult2 <- oneLevelNMF(X, rank=3, method="HALSacc", initData = W0)
Condition input data matrix properly for NMF
Description
Condition input data matrix properly for NMF
Usage
preProcesInputData(X)
Arguments
X |
input matrix |
Value
matrix with non-zero elements
Computation of relative NMF residual per observation
Description
Computation of relative NMF residual per observation
Usage
residualNMF(X, nmfFit)
Arguments
X |
Input data matrix, each column represents one observation |
nmfFit |
NMF model fitted to the input data in X |
Value
Relative residual per observation, returned as a vector
Author(s)
nsauwen
Apply fixed scaling to NMF model matrices by normalizing the basis vectors
Description
Apply fixed scaling to NMF model matrices by normalizing the basis vectors
Usage
scaleNMFResult(NMFResult)
Arguments
NMFResult |
Fitted NMF model |
Value
NMFResult Rescaled NMF model
Author(s)
Nicolas Sauwen
Semi-NMF based on multiplicative update rules. Reference: C. Ding, T. Li, and M.I. Jordan, "Convex and semi-nonnegative matrix factorizations", IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 45-55, 2010.
Description
Semi-NMF based on multiplicative update rules. Reference: C. Ding, T. Li, and M.I. Jordan, "Convex and semi-nonnegative matrix factorizations", IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 45-55, 2010.
Usage
semiNMF(X, nmfMod, maxiter = 2000, checkDivergence = FALSE)
Arguments
X |
Input data matrix, each column represents one observation and the rows correspond to the different features |
nmfMod |
Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition) |
maxiter |
Maximum number of iterations |
checkDivergence |
currently not in use, to be implemented |
Value
Resulting NMF model (in accordance with the NMF package definition)
Author(s)
nsauwen