Analyzing the Role of Sparse Matrix Computation in Large-Scale Statistical Modeling and Data Mining

Posted: Dec 25, 2024

• Angela Reed
• Anthony Parker
• Ariana Cooper

Abstract

The exponential growth of data in contemporary applications has presented unprecedented challenges for statistical modeling and data mining. Traditional computational approaches, designed primarily for dense data structures, often prove inadequate when confronted with the sparse, high-dimensional datasets characteristic of modern domains such as social network analysis, recommendation systems, and genomic research. Sparse matrices, where the majority of elements are zero, represent a fundamental data structure that can dramatically improve computational efficiency if properly leveraged. This research investigates the transformative potential of sparse matrix computation techniques in advancing the frontiers of large-scale statistical modeling and data mining. Current literature reveals a significant gap in comprehensive frameworks that systematically exploit sparsity across diverse statistical applications. While individual studies have demonstrated the benefits of sparse representations in specific contexts, a unified approach that spans multiple domains and methodologies remains elusive. The novelty of our work lies in developing a cross-domain framework that integrates quantum-inspired optimization principles with adaptive sparse matrix algorithms, creating a synergistic approach that transcends conventional computational boundaries.