A novel kernel-based method for local pattern extraction in random process signals

Abstract. We consider a class of random process signals which contain randomly position local similarities representing the texture of an object. Those repetitive parts may occur in speech, musical pieces and sonar signals. We suggest a warped time resolved spectrum kernel for extracting the subsequence similarity in time series in general, and as an example in biosonar signals. Having a set of those kernels for similarity extraction in different size of subsequences, we propose a new method to find an optimal linear combination and selection of those kernels. We formulate the optimal kernel selection via maximizing the Kernel Fisher Discriminant criterion (KFD) and use Mesh Adaptive Direct Search method (MADS) to solve the optimization problem. Our method is used for biosonar landmark classification with promising results.