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Conference Papers Year : 2017

Toward rank disaggregation: An approach based on linear programming and latent variable analysis

Abstract

This work presents an unsupervised approach to the problem of rank disaggregation, which can be defined as the task of decomposing a set of rankings provided by different people (or entities). To accomplish this task, we first discuss the problem of rank aggregation and how it can be solved via linear programming. Then, we introduce a disaggregation method based on rank aggregation and inspired by decomposition methods such as principal component analysis (PCA). The results are preliminary but may pave the way for a better understating of relevant features found in applications such as group decision.
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Dates and versions

hal-01484255 , version 1 (07-03-2017)

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Vincent Vigneron, Leonardo Tomazeli Duarte. Toward rank disaggregation: An approach based on linear programming and latent variable analysis. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. pp.192--200, ⟨10.1007/978-3-319-53547-0_19⟩. ⟨hal-01484255⟩
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