Streamed centering statistics for RKHS kernels — bigPLSR_stream

Compute the column means and grand mean of the kernel matrix \(K(X, X)\) without materialising it in memory. The input design matrix must be stored as a bigmemory::big.matrix (or descriptor), and the kernel is evaluated by iterating over row/column chunks.

Usage

bigPLSR_stream_kstats(
  Xbm,
  kernel,
  gamma,
  degree,
  coef0,
  chunk_rows = getOption("bigPLSR.predict.chunk_rows", 8192L),
  chunk_cols = getOption("bigPLSR.predict.chunk_cols", 8192L)
)

Arguments

Xbm: A bigmemory::big.matrix (or descriptor) containing the training design matrix.
kernel: Kernel name passed to stats::kernel() compatible helpers ("linear", "rbf", "poly", "sigmoid").
gamma, degree, coef0: Kernel hyper-parameters.
chunk_rows, chunk_cols: Numbers of rows/columns to process per chunk.

Value

A list with entries r (column means) and g (grand mean) of the kernel matrix.