{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:TFQOYISPX3HKU55BOSSOYNYEGH","short_pith_number":"pith:TFQOYISP","schema_version":"1.0","canonical_sha256":"9960ec224fbeceaa77a174a4ec370431e2819b7b2c62ef0169d2218a830f7c66","source":{"kind":"arxiv","id":"1511.04695","version":1},"attestation_state":"computed","paper":{"title":"An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NA","authors_text":"Bing Zeng, Hongbin Li, Jun Fang, Linxiao Yang","submitted_at":"2015-11-15T12:56:36Z","abstract_excerpt":"We consider the problem of low-rank decomposition of incomplete multiway tensors. Since many real-world data lie on an intrinsically low dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty fun"},"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":"1511.04695","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2015-11-15T12:56:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"852869a5e0338ad2805d638ce4507e4a5453a9d47c2533a9ad1893ef8754068e","abstract_canon_sha256":"4ea2e31c84ec445b87d3cebf939111467bfaa59cc306c8cc8a37c7b78456f6c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:12.729359Z","signature_b64":"N6nk9PBC4kmUx8NJStZruvkNmLkqfvA9V/DxErcecacSkOi4cV8eiGps8kwekHuT79Fj2YmbiZk4ChbE2UD8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9960ec224fbeceaa77a174a4ec370431e2819b7b2c62ef0169d2218a830f7c66","last_reissued_at":"2026-05-18T01:08:12.728872Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:12.728872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NA","authors_text":"Bing Zeng, Hongbin Li, Jun Fang, Linxiao Yang","submitted_at":"2015-11-15T12:56:36Z","abstract_excerpt":"We consider the problem of low-rank decomposition of incomplete multiway tensors. Since many real-world data lie on an intrinsically low dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty fun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.04695","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1511.04695","created_at":"2026-05-18T01:08:12.728954+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.04695v1","created_at":"2026-05-18T01:08:12.728954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.04695","created_at":"2026-05-18T01:08:12.728954+00:00"},{"alias_kind":"pith_short_12","alias_value":"TFQOYISPX3HK","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_16","alias_value":"TFQOYISPX3HKU55B","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_8","alias_value":"TFQOYISP","created_at":"2026-05-18T12:29:42.218222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH","json":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH.json","graph_json":"https://pith.science/api/pith-number/TFQOYISPX3HKU55BOSSOYNYEGH/graph.json","events_json":"https://pith.science/api/pith-number/TFQOYISPX3HKU55BOSSOYNYEGH/events.json","paper":"https://pith.science/paper/TFQOYISP"},"agent_actions":{"view_html":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH","download_json":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH.json","view_paper":"https://pith.science/paper/TFQOYISP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.04695&json=true","fetch_graph":"https://pith.science/api/pith-number/TFQOYISPX3HKU55BOSSOYNYEGH/graph.json","fetch_events":"https://pith.science/api/pith-number/TFQOYISPX3HKU55BOSSOYNYEGH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH/action/storage_attestation","attest_author":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH/action/author_attestation","sign_citation":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH/action/citation_signature","submit_replication":"https://pith.science/pith/TFQOYISPX3HKU55BOSSOYNYEGH/action/replication_record"}},"created_at":"2026-05-18T01:08:12.728954+00:00","updated_at":"2026-05-18T01:08:12.728954+00:00"}