There are three main command line binaries that can be used for generating a kernel or feature mapping, depending on how the base matrices are defined. They are listed here in order from the most to least general.
klcombinekernels
- given several kernel matrices for the same dataset, output a weighted combination of these kernel matrices.
klcombinefeatures
- given several explicit feature mappings of the same dataset, output a weighted combination of these feature mappings.
klweightfeatures
- given a single explicit feature mapping of the dataset, output a weighted feature mapping or kernel. I.e. each feature in this case corresponds to a rank-1 kernel.
$ command [flags] input_file output_file
The input_file
is either an explicit feature mapping representation of the data or a kernel matrix representation, both of which should follow the LIBSVM format.
The output_file
is also going to be an explicit feature mapping representation or kernel matrix representation of the data in LIBSVM format.
Here we give a list of useful flags that are used in conjunction with the command line binaries. Note, running any of the commands without any arguments will result in a full listing and description of possible flags and their default values.
--alg_reg
- For algorithm specific kernel combination methods, use the specified algorithm regularization parameter.
--ker_reg
- Specify kernel regularization parameter use within the kernel combination algorithm.
--interp_param
- The interpolation parameter used with iterative kernel combination algorithms, chosen between 0 and 1. A value closer to 0 will lead to larger steps, but also possible instability.
--lk_alg
- Select which kernel combination method is used. See feature table to see which combination methods are available with each command line binary.
--max_iter
- The maximum number of iterations used by any iterative kernel combination algorithm.
--mu_file
- Save the kernel combination weights to the specified file.
--num_train
- Give argument p:q
to select points p
through q
for training the matrix. The learned kernel function is still applied to the entire dataset.
--sparse
- Used sparse data-structures when given feature mappings as input.
--tol
- Specify tolerance of stopping criteria for iterative kernel combination algorithms. Smaller values lead to more precise answers, but longer convergence times.
--features
- print output using explicit feature mappings instead of computing the kernel.
--offset
- include the specified constant value as an additional feature (can be used to act as an offset).
-- AfshinRostamizadeh - 10 Sep 2009