Reliable spurious mode rejection using self learning algo- rithms

In this paper, we introduce a new technique for the separation of physical and spurious modes based on an initial clustering in frequency-damping space, followed by a self-learning classification algorithm. For the classification, Least Squares Support Vector Machines are used, a Least Squares version of the theory of Support Vector Machines which maps the classification problem to a high-dimensional feature space were the data points are linearly separable.