Low-rank methods for heterogeneous and multi-source data

Abstract : In modern applications of statistics and machine learning, one often encounters many data imperfections. In particular, data are often heterogeneous, i.e. combine quantitative and qualitative information, incomplete, with missing values caused by machine failure or nonresponse phenomenons, and multi-source, when the data result from the compounding of diverse sources. In this dissertation, we develop several methods for the analysis of multi-source, heterogeneous and incomplete data. We provide a complete framework, and study all the aspects of the different methods, with thorough theoretical studies, open source implementations, and empirical evaluations. We study in details two particular applications from ecology and medical sciences.
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Submitted on : Friday, June 28, 2019 - 2:58:12 PM
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Geneviève Robin. Low-rank methods for heterogeneous and multi-source data. Statistics [math.ST]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLX026⟩. ⟨tel-02168204⟩

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