Abstract
AbstractDimensionality 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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
