Extraction of food consumption systems by nonnegative matrix factorization (NMF) for the assessment of food choices.

Abstract:

:In Western countries where food supply is satisfactory, consumers organize their diets around a large combination of foods. It is the purpose of this article to examine how recent nonnegative matrix factorization (NMF) techniques can be applied to food consumption data to understand these combinations. Such data are nonnegative by nature and of high dimension. The NMF model provides a representation of consumption data through latent vectors with nonnegative coefficients, that we call consumption systems (CS), in a small number. As the NMF approach may encourage sparsity of the data representation produced, the resulting CS are easily interpretable. Beyond the illustration of its properties we provide through a simple simulation result, the NMF method is applied to data issued from a French consumption survey. The numerical results thus obtained are displayed and thoroughly discussed. A clustering based on the k-means method is also achieved in the resulting latent consumption space, to recover food consumption patterns easily usable for nutritionists.

journal_name

Biometrics

journal_title

Biometrics

authors

Zetlaoui M,Feinberg M,Verger P,Clémençon S

doi

10.1111/j.1541-0420.2011.01588.x

subject

Has Abstract

pub_date

2011-12-01 00:00:00

pages

1647-58

issue

4

eissn

0006-341X

issn

1541-0420

journal_volume

67

pub_type

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