This package includes Python implementations (with Numpy and Scipy) of numerical algorithms for computing nonnegative matrix factorization.
Numpy (http://www.numpy.org) and Scipy (http://www.scipy.org) need to be installed. Versions of Numpy and Scipy tested with this code were 1.6.1 and 0.9.0, respectively.
Non-Negative Matrix Factorization for dynamic brain networks, such that:
A ~= WH
Constraints:
A, W, H >= 0
L2-Regularization on W
L1-Sparsity on H
Implementation is based on :
1. Jingu Kim, Yunlong He, and Haesun Park. Algorithms for Nonnegative
Matrix and Tensor Factorizations: A Unified View Based on Block
Coordinate Descent Framework.
Journal of Global Optimization, 58(2), pp. 285-319, 2014.
2. Jingu Kim and Haesun Park. Fast Nonnegative Matrix Factorization:
An Active-set-like Method And Comparisons.
SIAM Journal on Scientific Computing (SISC), 33(6),
pp. 3261-3281, 2011.
Modified from: https://github.com/kimjingu/nonnegfac-python