Fast Large-Scale Spectral Clustering via Explicit Feature Mapping

We propose an efficient spectral clustering method for large-scale data. The main idea in our method consists of employing random Fourier features to explicitly represent data in kernel space. The complexity of spectral clustering thus is shown lower than existing Nystrom approximations on large-scale data. With in training points from a total of n data points, Nystrom method requires O(nmd + m(3) + nm(2)) operations, where d is the input dimension. In contrast, our proposed method requires O(nDd + D-3 + n'D-2), where n' is the number of data points needed until convergence and D is the kernel mapped dimension. In large-scale datasets where n' << n hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60000 data points), the proposed method is similar in clustering accuracy to Nystrom methods while its speed is twice as fast as Nystrom.