Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis

Author(s)

Vigneron, Vincent ; Duarte, Leonardo Tomazeli ; Tichavsky, P ; BabaieZadeh, M ; Michel, OJJ ; ThirionMoreau, N

• Document type: Journal article
• Source: LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017); v. 10169, p. 9-pg., 2017-01-01.

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. (AU)

• FAPESP's process: 15/16325-1 - Novel methods in multicreria decision analysis by means of advanced signal processing techniques
• Grantee: Leonardo Tomazeli Duarte
• Support Opportunities: Regular Research Grants