Evolutionary Unsupervised Kernel Regression

Dimension reduction and manifold learning play an important role in robotics, multimedia processing and data mining. For these tasks strong methods like Unsupervised Kernel Regression [4, 7] or Gaussian Process Latent Variable Models [5, 6] have been proposed in the last years. But many methods suffer from numerous local optima and crucial parameter dependencies. We use advanced methods from stochastic search to solve the embedded optimization problems of Unsupervised Kernel Regression. Furthermore, we apply a technique from Design of Experiments, i.e. Sequential Parameter Optimization, to tune the parameters and improve the algorithm’s performance.