Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation
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Learning sketching matrices for fast and accurate low-rank approximation (LRA) has gained increasing attention. Recently, Bartlett, Indyk, and Wagner (COLT 2022) presented a generalization bound for the learning-based LRA. Specifically, for rank-k approximation using an m × n learned sketching matrix with s non-zeros in each column, they proved an Õ(nsm) bound on the fat shattering dimension (Õ hides logarithmic factors). We build on their work and make two contributions. 1. We present a better Õ(nsk) bound (k ≤ m). En route to obtaining this result, we give a low-complexity Goldberg–Jerrum algorithm for computing pseudo-inverse matrices, which would be of independent interest. 2. We alleviate an assumption of the previous study that sketching matrices have a fixed sparsity pattern. We prove that learning positions of non-zeros increases the fat shattering dimension only by O(nslog n). In addition, experiments confirm the practical benefit of learning sparsity patterns.
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