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Analysis of nutrition data by means of a matrix factorization method


We present a factorization framework to analyze the data of a regression learning task with two peculiarities. First, inputs can be split into two parts that represent semantically significant entities. Second, the performance of regressors is very low. The basic idea of the approach presented here is to try to learn the ordering relations of the target variable instead of its exact value. Each part of the input is mapped into a common Euclidean space in such a way that the distance in the common space is the representation of the interaction of both parts of the input. The factorization approach obtains reliable models from which it is possible to compute a ranking of the features according to their responsibility in the variation of the target variable. Additionally, the Euclidean representation of data provides a visualization where metric properties have a clear semantics. We illustrate the approach with a case study: the analysis of a dataset about the variations of Body Mass Index for Age of children after a Food Aid Program deployed in poor rural communities in Southern México. In this case, the two parts of inputs are the vectorial representation of children and their diets. In addition to discovering latent information, the mapping of inputs allows us to visualize children and diets in a common metric space.

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The research reported here is supported in part under grant TIN2011-23558 from the MICINN (Ministerio de Ciencia e Innovación, Spain). Edna Gamboa was supported by a Ph.D. grant from CONACYT (Consejo Nacional de Ciencia y Tecnología, México). The paper was written while Antonio Bahamonde was visiting Cornell University with Grants of Movilidad Campus de Excelencia Internacional (Universidad de Oviedo) and of Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de Investigación (Ministerio de Educación, Cultura y Deporte, Spain). The dataset was gathered in a project supported by Ministerio de Desarrollo Social de México.

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Correspondence to Jorge Díez.

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Díez, J., Gamboa, E., González de Cossío, T. et al. Analysis of nutrition data by means of a matrix factorization method. Prog Artif Intell 3, 119–127 (2015).

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  • Matrix factorization
  • Learning to rank
  • Feature selection
  • Data analysis
  • Nutrition data
  • Body mass index (BMI)