NIMFA: A Python Library for Nonnegative Matrix Factorization

08/06/2018
by   Marinka Zitnik, et al.
4

NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. It supports both dense and sparse matrix representation. NIMFA's component-based implementation and hierarchical design should help the users to employ already implemented techniques or design and code new strategies for matrix factorization tasks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/02/2020

Image Analysis Based on Nonnegative/Binary Matrix Factorization

Using nonnegative/binary matrix factorization (NBMF), a matrix can be de...
research
12/03/2021

ALX: Large Scale Matrix Factorization on TPUs

We present ALX, an open-source library for distributed matrix factorizat...
research
09/14/2018

Identification of multi-scale hierarchical brain functional networks using deep matrix factorization

We present a deep semi-nonnegative matrix factorization method for ident...
research
04/10/2021

Adversarially-Trained Nonnegative Matrix Factorization

We consider an adversarially-trained version of the nonnegative matrix f...
research
05/16/2017

The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools...
research
05/26/2014

Fast and Robust Archetypal Analysis for Representation Learning

We revisit a pioneer unsupervised learning technique called archetypal a...
research
04/19/2018

Programming Parallel Dense Matrix Factorizations with Look-Ahead and OpenMP

We investigate a parallelization strategy for dense matrix factorization...

Please sign up or login with your details

Forgot password? Click here to reset