Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
This addresses the problem of high-dimensional data analysis for scientific domains like medical imaging, though it appears incremental as it builds on existing parallel and array programming concepts.
The paper tackles the computational challenges of applying deep learning to high-dimensional data by proposing a parallel computation architecture based on space completeness, which decomposes data into dimension-independent structures for distributed processing, enabling integration of data mining and machine learning methods across diverse data types.
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.