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Alternative TitleDimension reduction and visualization of mixed-type data based on E-t-SNE. Computer Engineering and Applications
魏世超1,2,3,4; 李歆1,3,4; 张宜弛1,3,4; 周晓锋1,3,4; 李帅1,2,3,4
Source Publication计算机工程与应用
Contribution Rank1
Funding Organization沈阳市科技计划项目(No.Z18-5-102)
Keywordt-SNE算法 混合属性数据 降维 可视化


Other Abstract

Aiming at the problem that the traditional t-SNE algorithm can only deal with single attribute data and can’t handle mixed type data very well. An extended t-SNE dimensionality reduction visualization algorithm named E-t-SNE is proposed. The extension facilitates to handle mixed type data. Firstly, the concept of information entropy is introduced to construct the distance matrix of categorical data. Secondly, the distance matrix of mixed type data is constructed by combining the distance between categorical data and the Euclidean distance of numerical data. Finally, the combined matrix is used into t-SNE algorithm to reduce the dimension and display it in two-dimensional space. In addition, in order to verify the effectiveness of the algorithm, K-Nearest Neighbor (KNN) algorithm is used to evaluate. Experiments on UCI datasets show that this method not only has good visualization ability in dealing with mixed attribute data, but also can effectively reduce the dimension of different classes of data and improve the classification accuracy of subsequent classifiers.

Document Type期刊论文
Corresponding Author魏世超
Recommended Citation
GB/T 7714
魏世超,李歆,张宜弛,等. 基于E-t-SNE的混合属性数据降维可视化方法[J]. 计算机工程与应用,2020,56(6):66-72.
APA 魏世超,李歆,张宜弛,周晓锋,&李帅.(2020).基于E-t-SNE的混合属性数据降维可视化方法.计算机工程与应用,56(6),66-72.
MLA 魏世超,et al."基于E-t-SNE的混合属性数据降维可视化方法".计算机工程与应用 56.6(2020):66-72.
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