Computes \(\hat Y_1 = X_1 (C_{+} - C_{-}) X_2 Y_2^{\mathrm{new}}\).
Since \(\Theta = C_{+} - C_{-}\) is signed, predictions may contain
negative entries even when \(Y_1 \ge 0\) in training.
Usage
# S3 method for class 'nmfae.signed'
predict(object, newY2 = NULL, Y1 = NULL, type = c("response", "class"), ...)
Arguments
- object
A fitted "nmfae.signed" object.
- newY2
New input matrix (P2 x N_new). If NULL, returns
the training fitted values.
- Y1
Optional reference Y1 for scatter / confusion plot.
- type
Output: "response" (raw signed) or "class".
- ...
Unused.
Value
A numeric matrix ("response") or factor ("class").
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.