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arXiv Machine Learning By Vincent Szolnoky

D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition

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The paper introduces D‑SLR, a closed‑form decomposition that combines a low‑rank approximation with a disjoint row‑sparse component, offering a drop‑in replacement for truncated SVD. Unlike previous joint methods, D‑SLR restricts each row to either be stored verbatim or approximated by the low‑rank fit, achieving the joint optimum without extra parameters and without iterative solvers or regularization tuning. The authors provide an assumption‑free, a‑posteriori error bound for every shape, and demonstrate performance gains on synthetic data, LLM embedding tables, network traffic, and hyperspectral images.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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