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<p>Data embedding, as one of the dimension reduction methods in visualization and classification proposed in recent years, aims at maintaining the complete information of original data so that the difference between the original and the embedded data is imperceptible. Stochastic neighbor embedding(SNE) as a nonlinear manifold learning algorithm has received extensive attention. Considering the multimodality of actual data and the crowding problems in SNE, we propose an improved stochastic neighbor embedding based on spherical logistic distribution on three-dimensional Euclidean space, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mathbf {SL_3}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi mathvariant="bold">SL</mi> <mn mathvariant="bold">3</mn> </msub> </math></EquationSource> </InlineEquation>-SNE. The technique is a variation of SNE that produces better clusterings by introducing spherical logistic distribution, which is more heavy-tailed than the normal distribution and is able to characterize the multimodality of data. Simulated and real experiment results show that the problems of crowding have been significantly alleviated, and the classification accuracy can be increased using the proposed algorithm in comparison to the existing t-SNE and vMF-SNE.</p>

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An improved SNE with its applications in classification and visualization

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Peilin Sun,
  • Xu Qin

摘要

Data embedding, as one of the dimension reduction methods in visualization and classification proposed in recent years, aims at maintaining the complete information of original data so that the difference between the original and the embedded data is imperceptible. Stochastic neighbor embedding(SNE) as a nonlinear manifold learning algorithm has received extensive attention. Considering the multimodality of actual data and the crowding problems in SNE, we propose an improved stochastic neighbor embedding based on spherical logistic distribution on three-dimensional Euclidean space, \(\mathbf {SL_3}\) SL 3 -SNE. The technique is a variation of SNE that produces better clusterings by introducing spherical logistic distribution, which is more heavy-tailed than the normal distribution and is able to characterize the multimodality of data. Simulated and real experiment results show that the problems of crowding have been significantly alleviated, and the classification accuracy can be increased using the proposed algorithm in comparison to the existing t-SNE and vMF-SNE.