| Abstract |
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 affected by the increase of feature dimension. |
Mindanao State
University - Iligan Institute of Technology