ISIDA/Kernel is a compilation of various kernel based methods. Those methods can be used to compute a Gram matrix, express a covariance matrix in the feature space, extract principal or important components. These methods are typicaly used to derive a "picture" of a dataset or to understand the characteristics of a feature space and relations with a particular property.
- Available on demand (Linux command line version, Windows + documentation)
- ISIDA/Kernel on Mobyle