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Table 2 Summary of machine learning dimensionality reduction methods used in this study

From: Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

Methods Descriptions
Principal component analysis (PCA) [44] PCA decomposes data into a set of successive orthogonal components that explain a maximum amount of the variance
Kernel PCA (KPCA) [45] KPCA extends PCA by using kernel functions to achieve non-linear dimensionality reduction
Latent semantic analysis (LSA) [46] LSA is similar to PCA but differs in that the data matrix does not need to be centered
Gaussian random projection (GRP) [47] GRP projects the original input features onto a randomly generated matrix where components are drawn from a Gaussian distribution
Sparse random projection (SRP) [48] SRP projects the original input features onto a sparse random matrix, which is an alternative to dense Gaussian random projection matrix
Multidimensional scaling (MDS) [49] MDS is a technique used for analyzing similarity or dissimilarity data, seeking a low-dimensional representation of the data in which the distances respect well the distances in the original high-dimensional space
Isomap [50] Isomap is a manifold learning algorithm, seeking a lower-dimensional embedding that maintains geodesic distances between all points
Locally linear embedding (LLE) [51] LLE projects the original input features to a lower-dimensional space by preserving distances within local neighborhoods