  Kernels are often developed and used as implicit mapping functions that show
impressive predictive power due to their high-dimensional feature space
representations. In this study, we gradually construct a series of simple
feature maps that lead to a collection of interpretable low-dimensional
kernels. At each step, we keep the original features and make sure that the
increase in the dimension of input data is extremely low, so that the resulting
discriminant functions remain interpretable and amenable to fast training.
Despite our persistence on interpretability, we obtain high accuracy results
even without in-depth hyperparameter tuning. Comparison of our results against
several well-known kernels on benchmark datasets show that the proposed kernels
are competitive in terms of prediction accuracy, while the training times are
significantly lower than those obtained with state-of-the-art kernel
implementations.
