Multi-class scatter diagram sampling method and system based on recursive division, storage medium and equipment

Abstract

The invention provides a multi-class scatter diagram sampling method and system based on recursive division, a storage medium and equipment. The multi-class scatter diagram sampling method comprises the steps that received multi-class scatter diagrams are converted into multi-class density diagrams; spatial recursion is performed according to density differences of adjacent regions on the multi-class density map, and a KD tree structure is divided; based on a kD tree structure, backtracking is carried out from all leaf nodes; points capable of keeping rare classes and nodes are found with relative class densities at the same time; recursively distributing class labels from the node; at least one point of each class is ensured, and the class density sequence is consistent with that before sampling; finally, one point with the distributed class label is selected from each leaf node. The relative data density and the relative class density can be rapidly calculated and maintained at the same time, the sampling results of the main outliers and the rare class points can be displayed, and the efficiency of analyzing the data visualized by the multi-class scatter diagram can be improved.

Description

technical field [0001] The disclosure belongs to the field of image information processing, and in particular relates to a method, system, storage medium and equipment for sampling multi-class scatter diagrams based on recursive division. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] A scatter plot (scatter plot) in regression analysis refers to the distribution of data points on a Cartesian coordinate plane. A scatter plot can effectively present variables by encoding data points into visual markers (for example, dots). correlations and outliers, as well as other patterns in the data. Also, by color-coding visual markers based on class labels, multiclass scatterplots are effective in visualizing data with class labels and observing correlations among multiple classes. In addition, many times, high-dimensional data reduced to 2D space...

AI Technical Summary

Problems solved by technology

Because large jitter may introduce false patterns that do not exist, which may lead to false perception by the observer, it has great limitations, and such methods cannot handle severe overdrawing

[0008] Do Density Estimation: This class of methods is an alternative to scatterplots that display discrete scatterplots as either a color-coded density map or a set of contour lines, in this way denser regions can be better characterized, However, outliers and sparse regions may be missed; moreover, visual observation of color-coded multi-class density fields is difficult due to color mixing, especially for overlapping regions.

[0009] Sampling optimization, this type of technology mainly solves the problem of overdrawing by optimizing the sampling process, mainly including random sampling, non-uniform sampling, blue noise sampling, etc. Random sampling can preserve dense areas and relative density differences, but it is easy to lose outliers Points and sparse areas; non-uniform sampling is easy to ignore the difference in different density areas; blue noise sampling imports special local patterns, and at the same time the processing speed is slow when the number of original data points is very large, and it will be unreasonable when the constructed density field is not good. the result of

[0010] To sum up, there are currently several ways to deal with overdrawing problems that have their own limitations.

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