overview of fast-gkm-svm
Existing gkm-SVM algorithms suffer from the slow kernel computation time, as they depend exponentially on the sub-sequence feature-length, number of mismatch positions, and the task’s alphabet size. In this project, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels. Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculation into a set of independent counting operations over the possible mismatch positions. This simplified decomposition allows us to devise a fast Monte Carlo approximation that rapidly converges. FastSK can scale to much greater feature lengths, allows us to consider more mismatches, and is performant on a variety of sequence analysis tasks.
Important wrappers we develop to help users
- GitHub: https://github.com/QData/FastSK
- (to come)
Thanks for reading!
Tool GaKCo-SVM: a Fast GApped k-mer string Kernel using COunting