Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming

• URL: http://arxiv.org/abs/2310.19068v1
• Date: Sun, 29 Oct 2023 16:46:26 GMT
• Title: Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
• Authors: Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda
• Abstract summary: We develop new techniques to extend the applicability of sketching-based approaches to sparse dictionary learning and the Euclidean $k$-means clustering problems. On the fast algorithms front, we obtain a new approach for designing PTAS's for the $k$-means clustering problem. On the streaming algorithms front, we obtain new upper bounds and lower bounds for dictionary learning and $k$-means clustering.
• Score: 48.18845814885398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sketching algorithms have recently proven to be a powerful approach both for designing low-space streaming algorithms as well as fast polynomial time approximation schemes (PTAS). In this work, we develop new techniques to extend the applicability of sketching-based approaches to the sparse dictionary learning and the Euclidean $k$-means clustering problems. In particular, we initiate the study of the challenging setting where the dictionary/clustering assignment for each of the $n$ input points must be output, which has surprisingly received little attention in prior work. On the fast algorithms front, we obtain a new approach for designing PTAS's for the $k$-means clustering problem, which generalizes to the first PTAS for the sparse dictionary learning problem. On the streaming algorithms front, we obtain new upper bounds and lower bounds for dictionary learning and $k$-means clustering. In particular, given a design matrix $\mathbf A\in\mathbb R^{n\times d}$ in a turnstile stream, we show an $\tilde O(nr/\epsilon^2 + dk/\epsilon)$ space upper bound for $r$-sparse dictionary learning of size $k$, an $\tilde O(n/\epsilon^2 + dk/\epsilon)$ space upper bound for $k$-means clustering, as well as an $\tilde O(n)$ space upper bound for $k$-means clustering on random order row insertion streams with a natural "bounded sensitivity" assumption. On the lower bounds side, we obtain a general $\tilde\Omega(n/\epsilon + dk/\epsilon)$ lower bound for $k$-means clustering, as well as an $\tilde\Omega(n/\epsilon^2)$ lower bound for algorithms which can estimate the cost of a single fixed set of candidate centers.

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