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Supervised t -Distributed Stochastic Neighbor Embedding for Data Visualization and Classification

Author

Listed:
  • Yichen Cheng

    (Institute for Insight, Georgia State University, Atlanta, Georgia 30303)

  • Xinlei Wang

    (Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275)

  • Yusen Xia

    (Institute for Insight, Georgia State University, Atlanta, Georgia 30303)

Abstract

We propose a novel supervised dimension-reduction method called supervised t-distributed stochastic neighbor embedding (St-SNE) that achieves dimension reduction by preserving the similarities of data points in both feature and outcome spaces. The proposed method can be used for both prediction and visualization tasks with the ability to handle high-dimensional data. We show through a variety of data sets that when compared with a comprehensive list of existing methods, St-SNE has superior prediction performance in the ultrahigh-dimensional setting in which the number of features p exceeds the sample size n and has competitive performance in the p ≤ n setting. We also show that St-SNE is a competitive visualization tool that is capable of capturing within-cluster variations. In addition, we propose a penalized Kullback–Leibler divergence criterion to automatically select the reduced-dimension size k for St-SNE. Summary of Contribution: With the fast development of data collection and data processing technologies, high-dimensional data have now become ubiquitous. Examples of such data include those collected from environmental sensors, personal mobile devices, and wearable electronics. High-dimensionality poses great challenges for data analytics routines, both methodologically and computationally. Many machine learning algorithms may fail to work for ultrahigh-dimensional data, where the number of the features p is (much) larger than the sample size n . We propose a novel method for dimension reduction that can (i) aid the understanding of high-dimensional data through visualization and (ii) create a small set of good predictors, which is especially useful for prediction using ultrahigh-dimensional data.

Suggested Citation

  • Yichen Cheng & Xinlei Wang & Yusen Xia, 2021. "Supervised t -Distributed Stochastic Neighbor Embedding for Data Visualization and Classification," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 566-585, May.
  • Handle: RePEc:inm:orijoc:v:33:y:2021:i:2:p:566-585
    DOI: 10.1287/ijoc.2020.0961
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    References listed on IDEAS

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    4. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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