Subsampling and aggregation: a solution to the scalability problem in distance based prediction for mixed-type data

The distance-based linear model (DB-LM) extends the classical linear regression to the framework of mixed-type predictors or when the only available information is a distance matrix between regressors (as it sometimes happens with big data). The main drawback of these DB methods is their computational cost, particularly due to the eigendecomposition of the Gram matrix. In this context, ensemble regression techniques provide a useful alternative to fitting the model to the whole sample. This work analyzes the performance of three subsampling and aggregation techniques in DB regression on two specific large, real datasets. We also analyze, via simulations, the performance of bagging and DB logistic regression in the classification problem with mixed-type features and large sample sizes.

big data; classification; dissimilarities; ensemble; generalized linear model; gower’s metric; machine learning

Subsampling and aggregation: a solution to the scalability problem in distance based prediction for mixed-type data Articles