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High-Dimensional Data Analysis for Social Media with Principal Component Analysis

Year: 2025

Type: article

Abstract: With the explosive growth of social media data, the need for effective high-dimensional data analysis techniques has become increasingly pressing. Current research in this field faces challenges such as data sparsity, noise, and scalability issues. To address these challenges, this paper proposes a novel approach utilizing Principal Component Analysis (PCA) for high-dimensional data analysis in social media. By applying PCA to reduce the dimensionality of the data while preserving essential information, our method aims to enhance the efficiency and accuracy of social media data analysis. Through comprehensive experimentation and evaluation, this paper demonstrates the effectiveness and potential of the proposed approach in improving the analysis of high-dimensional social media data, contributing valuable insights to the field of social media analytics.

Source: Optimizations in Applied Machine Learning

DOI: 10.71070/oaml.v5i1.130

Authors: Dmitry Ivanov, Elena Petrova, Alexei Smirnov

Institutions: Volgograd State Technical University, Ryazan State University, Ulyanovsk State Technical University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411130397

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