The Column Subset Selection Problem (CSSP) and the Nystrom method
are among the leading tools for constructing small low-rank
approximations of large datasets in machine learning and scientific
computing. A fundamental question in this area is: how well can a data subset of
size k compete with the best rank k approximation?
We develop techniques which exploit spectral properties of the data
matrix to obtain improved approximation guarantees which go beyond the
standard worst-case analysis.
Our approach leads to significantly better bounds for datasets with
known rates of singular value decay, e.g., polynomial or exponential decay.
Our analysis also reveals an intriguing phenomenon: the approximation factor as a function of k may exhibit multiple peaks and valleys, which we call a multiple-descent curve.
A lower bound we establish shows that this behavior is not an artifact of our analysis, but rather it is an inherent property of the CSSP and Nystrom tasks. Finally, using the example of a radial basis function (RBF) kernel, we show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.