Package 'nmfbin'

Logistic Non-negative Matrix Factorization

Description

This function performs Logistic Non-negative Matrix Factorization (NMF) on a binary matrix.

Usage

nmfbin(
  X,
  k,
  optimizer = "mur",
  init = "nndsvd",
  max_iter = 1000,
  tol = 1e-06,
  learning_rate = 0.001,
  verbose = FALSE,
  loss_fun = "logloss",
  loss_normalize = TRUE,
  epsilon = 1e-10
)

Arguments

X	A binary matrix (m x n) to be factorized.
k	The number of factors (components, topics).
optimizer	Type of updating algorithm. mur for NMF multiplicative update rules, gradient for gradient descent, sgd for stochastic gradient descent.
init	Method for initializing the factorization. By default Nonnegative Double Singular Value Decomposition with average densification.
max_iter	Maximum number of iterations for optimization.
tol	Convergence tolerance. The optimization stops when the change in loss is less than this value.
learning_rate	Learning rate (step size) for the gradient descent optimization.
verbose	Print convergence if TRUE.
loss_fun	Choice of loss function: logloss (negative log-likelihood, also known as binary cross-entropy) or mse (mean squared error).
loss_normalize	Normalize loss by matrix dimensions if TRUE.
epsilon	Constant to avoid log(0).

Value

A list containing:

• W: The basis matrix (m x k). The document-topic matrix in topic modelling.

• H: The coefficient matrix (k x n). Contribution of features to factors (topics).

• c: The global threshold. A constant.

• convergence: Divergence (loss) from X at every iter until tol or max_iter is reached.

Examples

## Not run: 
# Generate a binary matrix
m <- 100
n <- 50
X <- matrix(sample(c(0, 1), m * n, replace = TRUE), m, n)

# Set the number of factors
k <- 4

# Factorize the matrix with default settings
result <- nmfbin(X, k)

## End(Not run)

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