Skip to main content

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