Description: Compute kernel-based eigenvectors for a set of training examples.

Usage: gist-kpca [options] -train <filename>


Output: A tab-delimited matrix in which each column corresponds to an eigenvector. Eigenvectors are normalized so that the dot product of the eigenvector with itself equals the reciprocal of the corresponding eigenvalue. In the output, the eigenvectors are sorted by increasing magnitude.


By default, the base kernel function is a dot product. In this case, the kernel-pca will give the same results as a 'standard' principal component analysis. If desired, this kernel can be modified using the following options. The operations occur in the order listed below.

If the supplied kernel functions are insufficient, the user can supply as input a precalculated kernel matrix using the following option

The remaining options (except for -rdb) affect the output of the software.