Probabilistic Non-Negative Matrix Factorization with Binary Components

Matrix factorization with binary components

Motivated by an application in computational biology, we consider low-rank matrix factorization with {0, 1}-constraints on one of the factors and optionally convex constraints on the second one. In addition to the non-convexity shared with other matrix factorization schemes, our problem is further complicated by a combinatorial constraint set of size 2m·r, where m is the dimension of the data p...

Non-negative matrix factorization

Learning Probabilistic Relational Models Using Non-Negative Matrix Factorization

Probabilistic Relational Models (PRMs) are directed probabilistic graphical models representing a factored joint distribution over a set of random variables for relational datasets. While regular PRMs define probabilistic dependencies between classes’ descriptive attributes, an extension called PRM with Reference Uncertainty (PRM-RU) allows in addition to manage link uncertainty between them, b...

Separation of Reflection Components by Sparse Non-negative Matrix Factorization

This paper presents a novel method for separating reflection components in a single image based on the dichromatic reflection model. Our method is based on a modified version of sparse non-negative matrix factorization (NMF). It simultaneously performs the estimation of body colors and the separation of reflection components through optimization. Our method does not use a spatial prior such as ...

Non-negative Matrix Factorization with Sparseness Constraints

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, ...