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A New Micro-Batch Approach for Partial Least Square Clusterwise Regression

Abstract : Current implementations of Clusterwise methods for regression when applied to massive data either have prohibitive computational costs or produce models that are difficult to interpret. We introduce a new implementation Micro-Batch Clusterwise Partial Least Squares (mb-CW-PLS), which is consists of two main improvements: (a) a scalable and distributed computational framework and (b) a micro-batch Clusterwise regression using buckets (micro-clusters). With these improvements, we are able to produce interpretable regression models with multicollinearity within a reasonable time frame.
Keywords : PLS Clusterwise Spark
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Gaël Beck, Hanane Azzag, Stéphanie Bougeard, Mustapha Lebbah, Ndèye Niang. A New Micro-Batch Approach for Partial Least Square Clusterwise Regression. Procedia Computer Science, Elsevier, 2018, 144, pp.239-250. ⟨10.1016/j.procs.2018.10.525⟩. ⟨hal-02471601⟩



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