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Dimensionality reduction methodssuch as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), singular value decomposition (SVD), or non-negative matrix factorization (NMF) can be applied to reduce the dimension of the similarity matrix along with the statistical tests and feature rankings to represent the data with minimum information loss (Meng et al., 2016
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