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Results tagged with pca
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user 120161
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.
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Why does Andrew Ng prefer to use SVD and not EIG of covariance matrix to do PCA?
Also, this is not related to the main question, which is about PCA. The smallest components are ignored in PCA.
A similar argument can be made about numerical stability. … If I have to use the covariance matrix method for PCA, I would decompose it with eigh instead of svd. …
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