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Parallel factor based multidimensional data analysis method

A parallel factor and multi-dimensional data technology, applied in the field of signal analysis, can solve problems such as high algorithm complexity, high time complexity, and slow processing speed

Inactive Publication Date: 2015-12-16
WUHAN UNIV
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Problems solved by technology

However, the algorithmic complexity of parallel factors is as high as O(n 2 ), in the face of large-scale ultra-high-dimensional multi-dimensional data analysis, the processing speed is very slow, and it is difficult to meet the real-time needs of practical applications [Document 6]
[0004] Some of the above methods cannot solve the problem of high-dimensional data analysis, and some methods have high time complexity, which is difficult to meet the real-time requirements of many practical applications.

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[0077] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0078] Aiming at the problem that the traditional parallel factor algorithm cannot handle large-scale, high-dimensional multidimensional data analysis, the invention introduces an improved parallel factor algorithm for processing and analysis. By analyzing each step of the parallel factor algorithm, the present invention makes a large number of improvements to the parallel factor algorithm through the GPGPU (General Computing Graphics Processor) platform, thereby greatly improving the calculation speed of the algorithm and reducing the time complexity. The ma...

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Abstract

The invention discloses a parallel factor based multidimensional data analysis method. According to the method, each step of a parallel factor algorithm including Khatri-Rao product, Kronecker product and nonlinear iterative partial least square (NIPALS) algorithms is analyzed, and a large amount of parallelization processing is performed on the parallel factor algorithm through a general purpose graphic processing unit (GPGPU) platform, so that the operational speed of the algorithm is greatly increased and the time complexity is lowered. Through the improved parallel factor algorithm, the method can be applied to actual large-scale high-dimensional data analysis.

Description

technical field [0001] The invention belongs to the technical field of signal analysis and relates to a multidimensional data analysis method, in particular to a fast and expandable multidimensional data analysis method based on parallel factors. Background technique [0002] High-density multi-mode multi-dimensional data analysis has important research significance in scientific research and practical applications, such as multivariate neural signals. How to capture the characteristics of the signal without loss in the spatial domain, time domain and frequency domain of the signal is an urgent problem [Document 1]. In order to analyze the characteristics of large-scale data, [Document 2] proposed a principal component analysis (PCA, principal component analysis) method, which maps the high-dimensional data of the signal to a low-dimensional space through statistical knowledge to analyze the characteristics of the signal. In addition, [Document 3] proposed an independent co...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 陈丹李小俚胡阳阳蔡畅曾科闫佳庆邓泽
Owner WUHAN UNIV
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