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Scalable Kernel Logistic Regression with Nystr\"om Approximation: Theoretical Analysis and Application to Discrete Choice Modelling by Martín-Baos, José Ángel; García-Ródenas, Ricardo; Rodriguez-Benitez, Luis; Bierlaire, Michel is a scholarly article available to read on EtoBox.

What is Scalable Kernel Logistic Regression with Nystr\"om Approximation: Theoretical Analysis and Application to Discrete Choice Modelling about?

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers the efficient training of large-scale models. This paper addresses these problems of scalability by introducing the Nystr\"om approximation for Kernel Logistic Regression (KLR) on large datasets. The study begins by presenting a theoretical analysis in which: i) the set of KLR solutions is characterised, ii) an upper bound to the solution of KLR with Nystr\"om approximation is provided, and finally iii) a specialisation of the optimisation algorithms to Nystr\"om KLR is described. After this, the Nystr\"om KLR is computationally validated. Four landmark selection methods are tested, including basic uniform sampling, a k-means sampling strategy, and two non-uniform methods grounded in leverage scores. The performance of these strategies is evaluated using large-scale transport mode choice datasets and is compared with traditional methods such as Multinomial Logit (MNL) and contemporary ML techniques. The study also assesses the ef

Author

Martín-Baos, José Ángel; García-Ródenas, Ricardo; Rodriguez-Benitez, Luis; Bierlaire, Michel

Published

2024

Language

EN