We initiate the study of data dimensionality reduction, or sketching, for the $q\to p$ norms. Given an $n \times d$ matrix $A$, the $q\to p$ norm, denoted $\|A\|_{ q \to p} = \sup_{ x \in \mathbb{ R}^d \backslash \vec{ 0}} \frac{ \|Ax\|_p}{ \|x\|_q}$, is a natural generalization of several matrix and vector norms studied in the data stream and sketching models, with applications to datamining, hardness of approximation, and oblivious routing. We say a distribution $S$ on random matrices $L \in \mathbb{ R}^{ nd} \rightarrow \mathbb{ R}^k$ is a $(k,\alpha)$-sketching family if from $L(A)$, one can approximate $\|A\|_{ q \to p}$ up to a factor $\alpha$ with constant probability. We provide upper and lower bounds on the sketching dimension $k$ for every $p, q \in [1, \infty]$, and in a number of cases our bounds are tight. While we mostly focus on constant $\alpha$, we also consider large approximation factors $\alpha$, as well as other variants of the problem such as when $A$ has low rank.
Categories: cs.DS cs.CC