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fast large-scale spectral clustering via explicit feature mapping
2018年04月15日 浏览量：10668次 来源：
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 Nystr02m approximations on large-scale data. With m training points from a total of n data points, Nystr02m method requires O(nmd+m06+nm05) operations, where d is the input dimension. In contrast, our proposed method requires O(nDd+D06+n'D05), 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 (60,000 data points), the proposed method is similar in clustering accuracy to Nystr02m methods while its speed is twice as fast as Nystr02m
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