Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
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.
Chen Zhang
计算技术、计算机技术
Chen Zhang.Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration[EB/OL].(2025-06-28)[2025-07-16].https://arxiv.org/abs/2506.22929.点此复制
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