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Real world data analysis problems often require nonlinear methods to get successful prediction. Kernel methods, e.g. Kernelized Principal Component Analysis, are a common way to get nonlinear properties based on linear representations in a high-dimensional feature space. Unfortunately, traditional kernel methods are unscalable for large-size or even medium-size data. On the other hand, randomized algorithms have been recently proposed to extract nonlinear features in kernel methods. Compared with exact kernel methods, this family of approaches is capable of speeding up the training process dramatically, while maintaining acceptable the classification accuracy. However, these methods fail to engage discriminative features. This significantly limits their classification accuracy.

SDRNF: generating scalable and discriminative random nonlinear features from data