Archives for posts with tag: PCA

Dimension reduction and Low-dimensional embedding

Good slides explaning PCA, MDS, ISOMAP, and LLE.

PCA: Preserving variance.

MDS: Preserving pairwise distances.

ISOMAP: Nonlinear embedding.

LLE: Local neighborhood is linear, but globally non-linear.

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PCA with standardization in Matlab

By running [coeff,score,latent] = pca(X, ‘Centered’, true, ‘VariableWeights’, ‘variance’);, we can get the standardized PCA results. (The transformed observations are in score.)

When running just [coeff,score,latent] = pca(X);, only centering is applied.