nmf.rrr.signed.ecv: Element-wise Cross-Validation for Signed-Bottleneck NMF-AE

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Element-wise Cross-Validation for Signed-Bottleneck NMF-AE

Description

Element-wise k-fold cross-validation for nmfae.signed to select the decoder / encoder ranks (Q, R). Mirrors nmfae.ecv but uses the weighted Signed-Bottleneck NMF-AE fit path (Y1.weights): test-fold elements are zero-weighted during fitting, and held-out MSE is computed on those elements.

Usage

nmf.rrr.signed.ecv(
  Y1,
  Y2 = Y1,
  rank1 = 1:2,
  rank2 = NULL,
  ...,
  rank = NULL,
  rank.encoder = NULL
)

Arguments

Y1	Output matrix (P1 x N).
Y2	Input matrix (P2 x N). Default Y1.
rank1	Integer vector of candidate response-basis ranks. Default 1:2.
rank2	Integer vector of candidate covariate-basis ranks, or NULL (default: pair rank2 = rank1, diagonal grid).
...	Additional arguments: nfolds / div Number of folds. Default 5. seed RNG seed for fold assignment. Default 123. nstart Number of random restarts per fit. Default 1. Signed models have more local minima (the bottleneck can carry both signs), so nstart >= 10 is recommended for reproducible rank selection. Other args epsilon, maxit, warm.start, etc.\ are passed to nmfae.signed.
rank, rank.encoder	Deprecated aliases of rank1 / rank2.

Value

An object of class c("nmfae.signed.ecv", "nmfae.ecv") with objfunc (MSE per pair), sigma (RMSE), objfunc.fold (per-fold MSE), folds, QR, paired.

Lifecycle

This function is experimental. The interface may change in future versions.

References

Ding, C. H. Q., Li, T., & Jordan, M. I. (2010). Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 45–55.

See Also

nmfae.signed, nmfae.ecv

nmfkc documentation built on July 14, 2026, 1:07 a.m.