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Definition of "principal component analysis (PCA)" |
a technique applied to n-dimensional data vectors with the aim of finding a set of morthogonal vectors (typically m << n) that account as much as possible for the data’s variance. PCA can be carried out in an unsupervised fashion by implementing a normalized version of Hebb’s rule on m-linear neural units. |
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