用并行架构解决高维数据计算难题，支持科学级分析
Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
- 基于空间完备性分解高维数据为独立维度结构
- 实现数据挖掘与机器学习的无缝融合加速
- 适合医疗、自然图像等科学计算场景
深度学习在自然图像和语言处理中表现优异，但在高维数据上受维度诅咒制约。现有大规模数据工具侧重业务统计，缺乏数学统计支持。本文提出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无关结构，实现分布式处理。该框架可无缝集成数据挖掘与并行优化的机器学习方法，支持医疗影像、自然图像等多种数据类型的统一科学计算。
原文摘要 · Abstract (English)
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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