Kernel principal component analysis

In the field of multivariate statistics, kernel principal component analysis (kernel PCA)[1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space.

  1. ^ Schölkopf, Bernhard; Smola, Alex; Müller, Klaus-Robert (1998). "Nonlinear Component Analysis as a Kernel Eigenvalue Problem". Neural Computation. 10 (5): 1299–1319. CiteSeerX 10.1.1.100.3636. doi:10.1162/089976698300017467. S2CID 6674407.

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