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

Yichen Cheng (), Xinlei Wang () and Yusen Xia ()
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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

INFORMS Journal on Computing, 2021, vol. 33, issue 2, 566-585

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.

Keywords: classification; dimension size estimation; supervised dimension reduction; ultra-high dimension; visualization (search for similar items in EconPapers)
Date: 2021
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