Can you "project" new samples onto the L and S Matrices

[tinyheero]

Hi there,

Thanks for this package and your implementation of the randomized robust PCA method in the functon rrpca.

Once you have applied rrpca to some training data to learn the L and S matrices, is there a way to project new data onto these matrices to extract the corresponding L and S values for these new data? The rationale is to prevent the "re-learning" of the L and S matrix for each new data sample if I were to take the approach of just including the new sample in the input matrix to rrpca.

In other words, I am looking for something eqivalent of projecting a new sample onto an existing PCA space in a standard PCA analysis. For instance:

training_data <- USArrests[1:48, ]
new_data <- USArrests[49:50, ]
pr_out <- prcomp(training_data, scale = TRUE)
scale(new_data, pr_out$center, pr_out$scale) %*% pr_out$rotation

                PC1        PC2        PC3        PC4
Wisconsin 2.1059185 -0.6184669 -0.1558858  0.1897872
Wyoming   0.6759575  0.3035009 -0.2465021 -0.1636568

With these principal component (PC) values, I could then do something like subtracting them from the new data to clean up the input signal (assuming that the PC values represent noise in the system):

Is there an equivalent of this with rrpca with something like this:

rrpca_out <- rsvd::rrpca(training_data)
predict(rrpca_out, new_data)

Thanks!