 |
 |
Journal articles
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.
Cited literature [22 references]
https://hal-cnam.archives-ouvertes.fr/hal-02471601
Contributor : Ndeye Niang <>
Submitted on : Sunday, February 9, 2020 - 5:55:21 PM
Last modification on : Friday, December 4, 2020 - 6:04:03 PM
Long-term archiving on: : Sunday, May 10, 2020 - 1:24:06 PM
Contributor : Ndeye Niang <>
Submitted on : Sunday, February 9, 2020 - 5:55:21 PM
Last modification on : Friday, December 4, 2020 - 6:04:03 PM
Long-term archiving on: : Sunday, May 10, 2020 - 1:24:06 PM
File
1-s2.0-S1877050918322348-main....
Publisher files allowed on an open archive
Licence
Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License