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pca
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SVD and PCA: how the singular value decomposition does the same job
An SVD of the centered data matrix is PCA: V gives the axes, U times D the scores, and squared singular values over n - 1 the variances.
Read the answer →Why Euclidean distance breaks down in high dimensions
Why distances between points become nearly equal as features pile up, and how to tell if clustering on 100 features is still trustworthy.
Read the answer →Factor analysis vs PCA: what is the difference?
PCA compresses all the variance in your variables into components; factor analysis models only the shared variance as coming from hidden factors.
Read the answer →PCA, eigenvectors and eigenvalues explained in plain language
PCA finds the directions your data varies most. Eigenvectors are those directions; eigenvalues say how much variance each one carries.
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