Multiple kernel learning is becoming a popular method of boosting the performance of similarity based classifiers. It involves combining different kernels and finding the appropriate mixing parameters to achieve performance improvement. We focus on a multiple kernel learning (MKL) technique called lp-regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space de-noising on MKL. Experiments in image and video retrieval show that with both, the original kernels or de-noised kernels, lp MK-FDA outperforms its fixed-norm counterparts. Experiments also show that feature space de-noising boosts the performance of both single kernel FDA and lp MK-FDA. The methodology is applied to the problem of image database retrieval.
EventList powered by schlu.net