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KLMS
[W,FRAC] = KLMS(A,N)
[W,N] = KLMS(A,FRAC)
| Input | |
| A | Dataset |
| N | or FRAC Number of dimensions (>= 1) or fraction of variance (< 1) to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf. |
| Output | |
| W | Affine Karhunen-Loeve mapping |
| FRAC | or N Fraction of variance or number of dimensions retained. |
First a Karhunen Loeve Mapping is performed (i.e. PCA or MCA on the average prior-weighted class covariance matrix). The result is scaled by the mean class standard deviations. For N and FRAC, see KLM.
Default N: select all ('pre-whiten' the average covariance matrix, i.e. orthogonalize and scale). The resulting mapping has a unit average covariance matrix.
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