标题： 基于数组编程和并行加速的高维数据数学计算 显示英文标题

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

摘要： 虽然深度学习在自然图像和语言处理方面表现出色，但其在高维数据中的应用由于维度灾难面临计算挑战。 当前的大规模数据工具侧重于面向业务的描述性统计，缺乏对高级分析的数学统计支持。 我们提出了一种基于空间完备性的并行计算架构，将高维数据分解为维度独立的结构以进行分布式处理。 该框架实现了数据挖掘与并行优化机器学习方法的无缝集成，在统一系统中支持医疗和自然图像等多种数据类型的科学计算。 显示英文摘要

摘要： 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.