{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:37:29Z","timestamp":1773801449209,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Leveraging intrinsic data priors is critical for effective data recovery. However, existing approaches often struggle to achieve theoretical guarantees, strong performance, and computational efficiency simultaneously. In this paper, we introduce a novel Representative Coefficient Correlated Total Variation (RCCTV) regularizer that captures the recently observed low-rank and local smoothness properties of the representative coefficient tensor derived from a low-rank decomposition. RCCTV regularizer offers three key advantages: (1) it operates on a compact representative coefficient image significantly smaller than the original data, enabling highly efficient optimization; (2) it jointly enforces low-rankness and spatial smoothness through a single regularizer, eliminating the need for trade-off parameters; and (3) when integrated into a robust PCA framework (i.e., RCCTV-RPCA model), it admits provable exact recovery under mild conditions. To solve the resulting model, we develop an efficient ADMM-based algorithm accelerated via fast Fourier transform. Extensive experiments on both synthetic and real-world datasets demonstrate that the RCCTV-RPCA model achieves state-of-the-art accuracy while running significantly faster. Our code and Supplementary Material are available at https:\/\/github.com\/mendy-2013\/RCCTV.<\/jats:p>","DOI":"10.1609\/aaai.v40i6.42493","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:16:22Z","timestamp":1773789382000},"page":"4896-4904","source":"Crossref","is-referenced-by-count":0,"title":["Fast Guaranteed Robust Local-Smooth Principal Component Separation"],"prefix":"10.1609","volume":"40","author":[{"given":"Mingdi","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaijiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangjun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/42493\/46454","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/42493\/46454","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:16:22Z","timestamp":1773789382000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/42493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i6.42493","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}