Abstract

Principal component analysis is one of the most popular unsupervised learning methods for reducing the dimension of a given data set in a high-dimensional Euclidean space. However, computing principal components on a space of phylogenetic trees with fixed labels of leaves is a challenging task since a space of phylogenetic tree is not Euclidean. In 2017, Yoshida et al. defined a notion of tropical principal component analysis and they have applied it to a space of phylogenetic trees. The challenge, however, they encountered was a computational times.

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