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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 Connect in order to contact the contributor
Submitted on : Sunday, February 9, 2020 - 5:55:21 PM
Last modification on : Monday, February 21, 2022 - 3:38:18 PM
Long-term archiving on: : Sunday, May 10, 2020 - 1:24:06 PM
Contributor : Ndeye Niang Connect in order to contact the contributor
Submitted on : Sunday, February 9, 2020 - 5:55:21 PM
Last modification on : Monday, February 21, 2022 - 3:38:18 PM
Long-term archiving on: : Sunday, May 10, 2020 - 1:24:06 PM
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