Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition

Support Vector Machines (SVMs) are an important tool for performing classification on scattered data, where one usually has to deal with many data points in high-dimensional spaces. We propose solving SVMs in primal form using feature maps based on trigonometric functions or wavelets. In small dimensional settings the Fast Fourier Transform (FFT) and related methods are a powerful tool in order to deal with the considered basis functions. For growing dimensions the classical FFT-based methods become inefficient due to the …

anova classification cs.lg cs.na data deal feature form functions machines maps math.na primal small spaces support support vector machines tool vector