Nonnegative Principal Component Analysis (NPCA) is a variant of PCA where projection vectors - or, basis for learned subspace - contain no negative values.
do.npca(X, ndim = 2, preprocess = c("center", "scale", "cscale",
"decorrelate", "whiten"), maxiter = 1000, reltol = 1e-05)an \((n\times p)\) matrix or data frame whose rows are observations.
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess for more details.
number of maximum iteraions allowed.
stopping criterion for incremental relative error.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
zafeiriou_nonnegative_2010Rdimtools