Journal
| Title | High-dimensional sparse predictive modeling applied to varied correlation structure via the generalized additive model Posted by Bernadette Tubo |
| Authors | Bondaug, Farlley G. and Tubo, Bernadette F. |
| Publication date | 2023 |
| Journal | Procedia Computer Science 234 (2024) 70–78 |
| Volume | 234 |
| Pages | 70-78 |
| Publisher | Elsevier B.V. |
| Abstract | This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the Generalized Additive Model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not a ected by the increase of feature dimension. |
| Index terms / Keywords | principal components analysis; orthogonal PCA; super sparse PCA; generalized additive model |
| DOI | https://doi.org/10.1016/j.procs.2024.02.153 |
| URL | https://www.sciencedirect.com/science/article/pii/S1877050924003399/pdf?md5=ce7d3db5ed2fa93be7f9bbdb78499ddf&pid=1-s2.0-S1877050924003399-main.pdf |