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.
https://hal-cnam.archives-ouvertes.fr/hal-02471601
Contributor : Ndeye Niang
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Submitted on : Sunday, February 9, 2020 - 5:55:21 PM
Last modification on : Saturday, February 15, 2020 - 1:51:59 AM
