Learning relationships between data obtained independently

2016/01/04 by Alexandra Carpentier, Carpentier, Alexandra, Teresa Schlueter +1 · 5 citations
Computer Science · #Algorithms and Data Compression #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.1601.00504

openalex publication_date 2016/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The aim of this paper is to provide a new method for learning the relationships between data that have been obtained independently. Unlike existing methods like matching, the proposed technique does not require any contextual information, provided that the dependency between the variables of interest is monotone. It can therefore be easily combined with matching in order to exploit the advantages of both methods. This technique can be described as a mix between quantile matching, and deconvolution. We provide for it a theoretical and an empirical validation.

Citations

• An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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