High-dimensional time-variant data visualization method for enhancing visual perception in dimension-reduction space
A technology of time-varying data and visual perception, applied in visual data mining, electronic digital data processing, structured data retrieval, etc. Data timing characteristics and other issues, to achieve the effect of improving visualization and analysis efficiency, facilitating interactive analysis, and improving visual perception.
Active Publication Date: 2018-05-25
ZHEJIANG UNIV OF FINANCE & ECONOMICS
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However, existing visualization methods such as parallel coordinates, scatterplot matrix, and dimensionality reduction mapping techniques still have certain limitations. It is difficult to display the time series characteristics of data, which is not conducive to user interactive analysis, and hinders the visualization efficiency of high-dimensional time-varying data. promotion
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[0035] The method for visualizing high-dimensional time-varying data with enhanced visual perception in dimension-reduced space according to the present invention will be further described with specific embodiments below. The specific method is as follows:
[0036] Load the initial high-dimensional time-varying data, project the high-dimensional time-varying data into a two-dimensional space for display through the classic multi-dimensional scaling algorithm, and obtain the two-dimensional space coordinates of the high-dimensional time-varying data. The specific method is as follows:
[0037] First, the original high-dimensional time-varying data is normalized according to formula (2) to eliminate the influence of different dimensions.
[0038]
[0039] In formula (2), x i Represents the value of the i-th data in dimension x; x min , x max Respectively represent the maximum and minimum values of the dimension x; Indicates the i-th data item in dimension x after norm...
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The invention discloses a high-dimensional time-variant data visualization method for enhancing visual perception in dimension-reduction space. The method comprises the steps of reading high-dimensional time-variant data, using a multi-dimensional scaling algorithm to obtain two-dimensional space coordinates of the high-dimensional time-variant data, and using a formula (1) to solve the coordinates of the high-dimensional time-variant data to obtain a target orthogonal matrix, wherein the formula (1) is shown in the description, in the formula (1), Q represents the target orthogonal matrix andis shown in description, a formula shown in the description represents the two-dimensional space coordinates of the i-th piece of high-dimensional time-variant data at the current moment, a formula shown in the description represents the two-dimensional space coordinates of the i-th piece of high-dimensional time-variant data at the previous moment, N represents the amount of high-dimensional time-variant data, and Dt(Q) represents the projection offset of the high-dimensional time-variant data after the transformation of the orthogonal matrix at the current moment relative to the previous moment; the target orthogonal matrix is utilized to minimize the projection offset of the high-dimensional time-variant data in two-dimensional space, and the projection of the high-dimensional time-variant data at the current moment is obtained. By means of the method, the efficiency of visualizing and analyzing the high-dimensional time-variant data can be obviously improved.
Description
technical field [0001] The invention relates to a high-dimensional time-varying data visualization method in dimension reduction space, belonging to the technical field of computer graphics and data visualization. Background technique [0002] High-dimensional data analysis usually considers the multi-dimensional attributes of the data at the same time, especially when there is a strong correlation between the dimensions of the data, which cannot be considered separately, effectively display the multi-dimensional attributes of the data and use multi-dimensional attribute analysis to find potential features in the data. Research hotspots in the field of visual analysis. Parallel coordinates (Mcdonnell K T, Mueller K. Illustrative Parallel Coordi-nates [J]. Computer Graphics Forum, 2008, 27(3): 1031-1038.) represent high-dimensional data with polylines spanning a set of parallel vertical axes, where each The parallel axis represents a dimension. It supports users to extract ...
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IPC IPC(8): G06F17/30
CPCG06F16/2477G06F16/248G06F16/26
Inventor 周志光
Owner ZHEJIANG UNIV OF FINANCE & ECONOMICS