Theoretical insights into deep learning models in high-dimensional feature spaces: Performance and stability analysis

Abstract

This research investigates the delving parameters of the theoretical deep learning models in high-dimensional feature spaces and their delicate balance between performance and stability. The study starts with a comprehensive review of extant scholarship covering the action of deep learning in hugely populated settings. Then a theoretical approach explaining the influence of high-dimensionality on the behavioral aspects of models, especially in regard to the proposed ratio of stability and performance, is provided. Moreover, these approaches are theoretical and mathematical with the aim of providing the model stability under these conditions. The study also searches phenomenal extensions of computer vision and data modeling using the principles learned in this article.