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nonnegfac's Introduction

Python Toolbox for Nonnegative Matrix Factorization

This package includes Python implementations (with Numpy and Scipy) of numerical algorithms for computing nonnegative matrix factorization.

Requirements

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.

Quick-Start

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

nonnegfac's People

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