Scalable Alignment Kernels via Space-Efficient Feature Maps.

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVMs in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We present the first approximation named ESP+SFM for alignment kernels by leveraging a metric embedding named edit-sensitive parsing (ESP) and space-efficient feature maps (SFM) for random Fourier features (RFF) for large-scale string analyses. Input strings are projected into vectors of RFF by leveraging ESP and SFM. Then, SVMs are trained on the projected vectors, which enables to significantly improve the scalability of alignment kernels while preserving their prediction accuracies. We experimentally test ESP+ SFM on its ability to learn SVMs for large-scale string classifications with various massive string data, and we demonstrate the superior performance of ESP+SFM with respect to prediction accuracy, scalability and computation efficiency.