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.
This research was supported by the program Cátedras Franco-Brasileiras no Estado de São Paulo, an initiative of the French consulate and the state of São Paulo (Brazil). We thank our colleagues Prof. João M. T. Romano, Dr. Kenji Nose and Dr. Michele Costa, who provided insights that greatly assisted this work. L.T. Duarte thanks the São Paulo Research Foundation (FAPESP) (Grant 2015/16325-1) and the National Council for Scientific and Technological Development (CNPq) for funding his research.
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Vigneron, V., Duarte, L.T. (2017). Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_19
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DOI: https://doi.org/10.1007/978-3-319-53547-0_19
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