Out-of-sample data visualization using bi-kernel t-SNE

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Information Visualization Pub Date : 2020-12-08 DOI:10.1177/1473871620978209
Haili Zhang, Pu Wang, Xuejin Gao, Yongsheng Qi, Huihui Gao
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引用次数: 4

Abstract

T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.
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基于双核t-SNE的样本外数据可视化
t分布随机邻居嵌入(t-SNE)是一种有效的可视化方法。然而,它是非参数的,不能应用于蒸汽数据或在线场景。尽管内核t-SNE提供了从高维数据空间到低维特征空间的显式投影,但一些异常值不能很好地投影。本文提出了用于样本外数据可视化的双核t-SNE。使用输入空间和特征空间的高斯核矩阵来近似显式投影。然后利用主成分分析对特征核矩阵进行降维。因此,内线和离群值之间的差异被揭示出来。任何新的样本都可以被很好地绘制出来。通过与其他先进算法的比较,在多个基准数据集上测试了该方法对样本外投影的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Visualization
Information Visualization COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.40
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice. This journal is a member of the Committee on Publication Ethics (COPE).
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