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Very interesting. It would have been even better if there had been a link to an explanation of how PCA is performed.
by kwhitefoot 6y ago
Very interesting. It would have been even better if there had been a link to an explanation of how PCA is performed.
- baron_harkonnen 6y agoIf you know a bit of linear algebra the transformation is surprisingly intuitive. Your goal is to create a set of orthogonal vectors, each that captures the highest amount of variance in the original data (the assumption is that variance is where the most information is). This is achieved by performing Eigen-decomposition on the Covariance matrix of the original data. Essentially you are learning the eigenvectors of the covariance matrix, ordered by their eigenvalues.
- bigdict 6y agoOr the singular vectors of the zero-centered data, ordered by singular values.