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Results tagged with pca
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user 28055
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.
12
votes
Making sense of principal component analysis, eigenvectors & eigenvalues
Non-Mathy:
The non-math explanation is that PCA helps for high dimensional data by letting you see in which directions your data has the most variance. These directions are the principal components. … Then in desperation you think to run PCA and discover the 90% of your variance comes from one direction, and that direction does not correspond to any of your axis. …
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