On some graph-based two-sample tests for high dimension, low sample size data

• Published in: Machine learning Vol. 109; no. 2; pp. 279 - 306
• Main Authors: Sarkar, Soham, Biswas, Rahul, Ghosh, Anil K.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-02-2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Computer simulation; Control; Euclidean geometry; Mechatronics; Natural Language Processing (NLP); Robotics; Simulation and Modeling; Statistical tests; High-dimensional consistency; Permutation test; Minimum spanning tree; Nearest neighbor; Distance concentration; Shortest Hamiltonian path; Non-bipartite matching
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-019-05857-4

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are computed using pairwise Euclidean distances among the observations. But, due to concentration of pairwise Euclidean distances, these tests have poor performance in many high-dimensional problems. Some of them can have powers even below the nominal level when the scale-difference between two distributions dominates the location-difference. To overcome these limitations, we introduce some new dissimilarity indices and use them to modify some popular graph-based tests. These modified tests use the distance concentration phenomenon to their advantage, and as a result, they outperform the corresponding tests based on the Euclidean distance in a wide variety of examples. We establish the high-dimensional consistency of these modified tests under fairly general conditions. Analyzing several simulated as well as real data sets, we demonstrate their usefulness in high dimension, low sample size situations.