High-dimensional data visualization method based on probability multi-level graph structure

A technology of high-dimensional data and graph structure, applied in the fields of instrument, character and pattern recognition, integrated learning, etc., can solve the problems of time-consuming optimization process, unsatisfactory visualization effect, difficult to handle large-scale data, etc., and achieve a good algorithm. Effects of complexity, beautiful visualizations
CN112163641AActive Publication Date: 2021-01-01ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG UNIV
Publication Date
2021-01-01

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Abstract

The invention relates to a high-dimensional data visualization method based on a probability multi-level graph structure, and belongs to the technical field of data visualization and dimension reduction. The high-dimensional data visualization method comprises the steps of: (1) giving a high-dimensional data set, wherein the data set comprises n data points, and the dimension of each data point isD; 2) calculating k neighbors of each data point, constructing a nearest neighbor graph structure G0, and constructing a probability multi-level graph structure based on the graph structure G0 to obtain a probability multi-level graph structure set; 3) laying out probability multi-level graphs layer by layer based on the probability multi-level graph structure set to obtain data low-dimensional representation, wherein the dimension of each data point is two-dimensional or three-dimensional; and 4) constructing a scatter view based on the low-dimensional data for data mining and analysis. According to the high-dimensional data visualization method, an optimization calculation process is accelerated by utilizing a hierarchical graph structure, and a visualization effect is optimized by introducing probability-based sampling.
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Description

technical field

[0001] The invention relates to the technical field of data visualization and dimensionality reduction, in particular to a high-dimensional data visualization method based on a probabilistic multi-level graph structure. Background technique

[0002] High-dimensional data visualization is an important task in data analysis, and plays a vital role in deep learning, life science and network analysis. Dimensionality reduction algorithms learn complex information in data, transform high-dimensional data into low-dimensional data, and analyze the distribution of data.

[0003] Over the past few decades, a large number of visualization methods for high-dimensional data have been proposed. The t-SNE algorithm is one of the most successful dimensionality reduction algorithms. The invention patent application document with the publication number CN110458187A discloses a malicious code family clustering method and system, wherein the method includes using the T-SNE alg...

Claims

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