Description: Project a given set of data onto a given set of eigenvectors.
Usage: project [options] -train <filename> -learned <filename> -test <filename>
Input:
- -train <filename> - an RDB file of training data. The first column contains labels, and the remaining columns containing real-valued features.
- -learned <filename> - an RDB file of eigenvectors, as produced by kernel-pca. Each column corresponds to an eigenvector.
- -test <filename> - an RDB file of test data to be projected onto the training data's eigenvectors.
Output: An RDB matrix in which the given data has been projected onto the given set of eigenvectors.
Options:
- -selftrain <filename> - Read from the given file a series of n values of the form K(x,x), where K is the base kernel function and x is an element in the training set. This option is only necessary if the base kernel function is supplied from a file and the kernel is normalized or radial basis. The input file should be in RDB format, with data labels in the first column and values in the second column.
- -selftest <filename> - Similar to '-selftrain', but for the test set.
- -noformatline - Usually, RDB formatted files contain column width information on the second line of the file. With this option, the program does not expect a format line in the input files and does not produce a format line in the output file.
- -kernelout - Compute and print the kernel matrix to stdout. Do not compute the classifications.
- -notime - Do not include timing information in the output header.
- -verbose 1|2|3|4|5 - Set the verbosity level of the output to stderr. The default level is 2.