{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QQORAXNKVCVUVO2GMJLIR2BVNH","short_pith_number":"pith:QQORAXNK","schema_version":"1.0","canonical_sha256":"841d105daaa8ab4abb46625688e83569e75d4d51f9e3ec6eedf508b2fddb2aa9","source":{"kind":"arxiv","id":"2401.11940","version":3},"attestation_state":"computed","paper":{"title":"Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Xi-Le Zhao, Yandong Tang, Yao Wang, Zhi Han, Zhiyu Liu","submitted_at":"2024-01-22T13:30:11Z","abstract_excerpt":"This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements. Traditional approaches tackling such a problem require the computation of tensor Singular Value Decomposition (t-SVD), that is a computationally intensive process, rendering them impractical for dealing with large-scale tensors. Aim to address this challenge, we propose an efficient and effective low-tubal-rank tensor recovery method based on a factorization procedure akin to the Burer-Monteiro (BM) method. Precisely, our fundamental approach"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.11940","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-22T13:30:11Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"507a3f914685a5f4005beb4cbec22c0394c04cf2fd919a5ba347cb8ee902bf83","abstract_canon_sha256":"5ee99ea80ffaa0190b256ccd1650e6682393737e157267e720d6e977ae734afc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:14.076155Z","signature_b64":"LbJsFUwE+2bO7LXZQ0id4XPiiwtEMaL5+WoxRS/TFszu4oDf/JMW5MD12icJYIhQLzUhN6HWaoXRs2RSN2SpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"841d105daaa8ab4abb46625688e83569e75d4d51f9e3ec6eedf508b2fddb2aa9","last_reissued_at":"2026-07-05T09:59:14.075668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:14.075668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Xi-Le Zhao, Yandong Tang, Yao Wang, Zhi Han, Zhiyu Liu","submitted_at":"2024-01-22T13:30:11Z","abstract_excerpt":"This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements. Traditional approaches tackling such a problem require the computation of tensor Singular Value Decomposition (t-SVD), that is a computationally intensive process, rendering them impractical for dealing with large-scale tensors. Aim to address this challenge, we propose an efficient and effective low-tubal-rank tensor recovery method based on a factorization procedure akin to the Burer-Monteiro (BM) method. Precisely, our fundamental approach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.11940","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.11940/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.11940","created_at":"2026-07-05T09:59:14.075737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.11940v3","created_at":"2026-07-05T09:59:14.075737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.11940","created_at":"2026-07-05T09:59:14.075737+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQORAXNKVCVU","created_at":"2026-07-05T09:59:14.075737+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQORAXNKVCVUVO2G","created_at":"2026-07-05T09:59:14.075737+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQORAXNK","created_at":"2026-07-05T09:59:14.075737+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.04565","citing_title":"Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH","json":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH.json","graph_json":"https://pith.science/api/pith-number/QQORAXNKVCVUVO2GMJLIR2BVNH/graph.json","events_json":"https://pith.science/api/pith-number/QQORAXNKVCVUVO2GMJLIR2BVNH/events.json","paper":"https://pith.science/paper/QQORAXNK"},"agent_actions":{"view_html":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH","download_json":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH.json","view_paper":"https://pith.science/paper/QQORAXNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.11940&json=true","fetch_graph":"https://pith.science/api/pith-number/QQORAXNKVCVUVO2GMJLIR2BVNH/graph.json","fetch_events":"https://pith.science/api/pith-number/QQORAXNKVCVUVO2GMJLIR2BVNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH/action/storage_attestation","attest_author":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH/action/author_attestation","sign_citation":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH/action/citation_signature","submit_replication":"https://pith.science/pith/QQORAXNKVCVUVO2GMJLIR2BVNH/action/replication_record"}},"created_at":"2026-07-05T09:59:14.075737+00:00","updated_at":"2026-07-05T09:59:14.075737+00:00"}