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