Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration

Chen Zhang
6/28/2025
cs.LGcs.AIeess.IV

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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.

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Cite this paper

@article{zhang2025mathematical,
  title  = {Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration},
  author = {Chen Zhang},
  year   = {2025},
  eprint = {2506.22929},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.22929v1}
}

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