Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
Machine Learning
2025-07-01 v1 Artificial Intelligence
Image and Video Processing
Signal Processing
Abstract
While deep learning excels in natural image and language processing, its application to high-dimensional data faces computational challenges due to the dimensionality curse. Current large-scale data tools focus on business-oriented descriptive statistics, lacking mathematical statistics support for advanced analysis. We propose a parallel computation architecture based on space completeness, decomposing high-dimensional data into dimension-independent structures for distributed processing. This framework enables seamless integration of data mining and parallel-optimized machine learning methods, supporting scientific computations across diverse data types like medical and natural images within a unified system.
Cite
@article{arxiv.2506.22929,
title = {Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration},
author = {Chen Zhang},
journal= {arXiv preprint arXiv:2506.22929},
year = {2025}
}