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Journal Articles Procedia Computer Science Year : 2018

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.

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Dates and versions

hal-02471601 , version 1 (09-02-2020)

Licence

Attribution - NonCommercial - NoDerivatives

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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, 2018, 144, pp.239-250. ⟨10.1016/j.procs.2018.10.525⟩. ⟨hal-02471601⟩
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