Approximate the embedding of a new data point using the Nystrom method, which is particularly useful for large datasets and data-dependent embedding spaces, such as multidimensional scaling (MDS).
nystrom_embedding(
new_data,
landmark_data,
kernel_function,
eigenvectors,
eigenvalues,
...
)A matrix containing the approximate embedding of the new_data in the data-dependent space.
A matrix or data frame containing the new data points to be projected.
A matrix or data frame containing the landmark data points used for approximation.
A function used to compute the kernel matrix (e.g., a distance function for MDS).
A matrix containing the eigenvectors obtained from the eigendecomposition of the kernel matrix between the landmark points.
A vector containing the eigenvalues obtained from the eigendecomposition of the kernel matrix between the landmark points.
Additional arguments passed to the kernel_function.