@inproceedings{0896955e36b3402492dae9a0efe9574f,
title = "Tight Sensitivity Bounds for Smaller Coresets",
abstract = "An ϵ-coreset to the dimensionality reduction problem for a (possibly very large) matrix A ĝ Rn x d is a small scaled subset of its n rows that approximates their sum of squared distances to every affine k-dimensional subspace of Rd, up to a factor of 1±ϵ. Such a coreset is useful for boosting the running time of computing a low-rank approximation (k-SVD/k-PCA) while using small memory. Coresets are also useful for handling streaming, dynamic and distributed data in parallel. With high probability, non-uniform sampling based on the so called leverage score or sensitivity of each row in A yields a coreset. The size of the (sampled) coreset is then near-linear in the total sum of these sensitivity bounds. We provide algorithms that compute provably tight bounds for the sensitivity of each input row. It is based on two ingredients: (i) iterative algorithm that computes the exact sensitivity of each row up to arbitrary small precision for (non-affine) k-subspaces, and (ii) a general reduction for computing a coreset for affine subspaces, given a coreset for (non-affine) subspaces in Rd. Experimental results on real-world datasets, including the English Wikipedia documents-term matrix, show that our bounds provide significantly smaller and data-dependent coresets also in practice. Full open source code is also provided.",
keywords = "PCA, SVD, coreset, dimensionality reduction, low rank approximation, sketch",
author = "Alaa Maalouf and Adiel Statman and Dan Feldman",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 ; Conference date: 23-08-2020 Through 27-08-2020",
year = "2020",
month = aug,
day = "23",
doi = "https://doi.org/10.1145/3394486.3403256",
language = "American English",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "2051--2061",
booktitle = "KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
address = "United States",
}