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Results tagged with pca
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user 7
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
56
votes
Making sense of principal component analysis, eigenvectors & eigenvalues
PCA is a technique to reduce dimension by:
Taking linear combinations of the original variables.
Each linear combination explains the most variance in the data it can. … I would never try to explain this to my grandmother, but if I had to talk generally about dimension reduction techniques, I'd point to this trivial projection example (not PCA). …
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