Statistical Learning Theory and Algorithm Optimization for High Dimensional Data

Abstract

The goal of this study is to deeply study the theoretical foundation and algorithm improvement technology of high-dimensional data statistical learning in order to meet the challenges of high-dimensional data in contemporary science, engineering, economics and other fields. Firstly, this paper expounds the universality of high-dimensional data and the limitations of traditional statistical learning methods in dealing with such data, and emphasizes the importance and practical application value of studying the statistical learning theory and algorithm optimization of high-dimensional data. Then the basic theory of statistical learning of high-dimensional data is comprehensively reviewed, including the characteristics and challenges of high-dimensional data, the basic concepts of statistical learning theory and statistical learning methods suitable for high-dimensional data. Based on the above, a series of algorithm optimization strategies for high-dimensional data processing are proposed, including feature selection and dimension reduction technology, parallel and distributed computing technology, and the effectiveness of these strategies is verified by empirical research. The research results show that the proposed algorithm optimization technology significantly improves the accuracy, stability and computational efficiency of high-dimensional data processing.