| Abstract: | Dimensionality reduction is an important technique in surrogate modeling and machine learning. In this article, we propose a supervised dimensionality reduction method, “least squares regression principal component analysis” (LSR-PCA), applicable to both classification and regression problems. To show the efficacy of this method, we present different examples in visualization, classification, and regression problems, comparing it with several state-of-the-art dimensionality reduction methods. Finally, we present a kernel version of LSR-PCA for problems where the inputs are correlated nonlinearly. The examples demonstrate that LSR-PCA can be a competitive dimensionality reduction method. |
