{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:XZKAZ6N74J4RC4AL7XJDN3NIPK","short_pith_number":"pith:XZKAZ6N7","canonical_record":{"source":{"id":"1904.02580","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-04T14:32:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d9e955056f0de7cf4621480183234fcd9414611f2a296d810eb932fd42c29c19","abstract_canon_sha256":"b66ab85d1dd2753661a442da923034c0aeb4a462f866f3ccd9e41b62d9ee5c43"},"schema_version":"1.0"},"canonical_sha256":"be540cf9bfe27911700bfdd236eda87a8d9073bfa1e5ebda3761b67a0c7fa3a5","source":{"kind":"arxiv","id":"1904.02580","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.02580","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"arxiv_version","alias_value":"1904.02580v2","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.02580","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"pith_short_12","alias_value":"XZKAZ6N74J4R","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XZKAZ6N74J4RC4AL","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XZKAZ6N7","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:XZKAZ6N74J4RC4AL7XJDN3NIPK","target":"record","payload":{"canonical_record":{"source":{"id":"1904.02580","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-04T14:32:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d9e955056f0de7cf4621480183234fcd9414611f2a296d810eb932fd42c29c19","abstract_canon_sha256":"b66ab85d1dd2753661a442da923034c0aeb4a462f866f3ccd9e41b62d9ee5c43"},"schema_version":"1.0"},"canonical_sha256":"be540cf9bfe27911700bfdd236eda87a8d9073bfa1e5ebda3761b67a0c7fa3a5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:47.321621Z","signature_b64":"i49/em4oh01yO6Gxh71NX7HuvP9lsfE1nUaDv+s5O1xkMGJx/y0u5tvwjj6sFx0CvyojHxXEIdx3ktyx5NYOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be540cf9bfe27911700bfdd236eda87a8d9073bfa1e5ebda3761b67a0c7fa3a5","last_reissued_at":"2026-05-17T23:44:47.320926Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:47.320926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.02580","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n8aotf9AE0odI9NldYb8YdC3bOFXNZu8VI1wM0ZQROrdzJ5l8vrWN70rbHJYc16oXsPtXNyxAeg3RIYRSOLrAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T05:06:54.950789Z"},"content_sha256":"3bc6cfddda2a70a29a8c7ff1dba7c285aa81749193b362c47da409a34780043c","schema_version":"1.0","event_id":"sha256:3bc6cfddda2a70a29a8c7ff1dba7c285aa81749193b362c47da409a34780043c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:XZKAZ6N74J4RC4AL7XJDN3NIPK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Convex Matrix Factorization with Representative Regions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhishek Agarwal, Jianhao Peng, Olgica Milenkovic","submitted_at":"2019-04-04T14:32:19Z","abstract_excerpt":"Matrix factorization (MF) is a versatile learning method that has found wide applications in various data-driven disciplines. Still, many MF algorithms do not adequately scale with the size of available datasets and/or lack interpretability. To improve the computational efficiency of the method, an online (streaming) MF algorithm was proposed in Mairal et al. [2010]. To enable data interpretability, a constrained version of MF, termed convex MF, was introduced in Ding et al. [2010]. In the latter work, the basis vectors are required to lie in the convex hull of the data samples, thereby ensuri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.02580","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YpC/N60tiiMkML+QgrPhj6tuKit/DrXY3pbO68Ma8SLImtwNXl8KlbSY+bSpOkPNuWJmK80Bjx3LROJ/txsQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T05:06:54.951128Z"},"content_sha256":"ee46d692038d91aba115665d9ff537e8a5067ad8adc02179309e674621c26cd6","schema_version":"1.0","event_id":"sha256:ee46d692038d91aba115665d9ff537e8a5067ad8adc02179309e674621c26cd6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/bundle.json","state_url":"https://pith.science/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T05:06:54Z","links":{"resolver":"https://pith.science/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK","bundle":"https://pith.science/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/bundle.json","state":"https://pith.science/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XZKAZ6N74J4RC4AL7XJDN3NIPK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:XZKAZ6N74J4RC4AL7XJDN3NIPK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b66ab85d1dd2753661a442da923034c0aeb4a462f866f3ccd9e41b62d9ee5c43","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-04T14:32:19Z","title_canon_sha256":"d9e955056f0de7cf4621480183234fcd9414611f2a296d810eb932fd42c29c19"},"schema_version":"1.0","source":{"id":"1904.02580","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.02580","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"arxiv_version","alias_value":"1904.02580v2","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.02580","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"pith_short_12","alias_value":"XZKAZ6N74J4R","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XZKAZ6N74J4RC4AL","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XZKAZ6N7","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:ee46d692038d91aba115665d9ff537e8a5067ad8adc02179309e674621c26cd6","target":"graph","created_at":"2026-05-17T23:44:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Matrix factorization (MF) is a versatile learning method that has found wide applications in various data-driven disciplines. Still, many MF algorithms do not adequately scale with the size of available datasets and/or lack interpretability. To improve the computational efficiency of the method, an online (streaming) MF algorithm was proposed in Mairal et al. [2010]. To enable data interpretability, a constrained version of MF, termed convex MF, was introduced in Ding et al. [2010]. In the latter work, the basis vectors are required to lie in the convex hull of the data samples, thereby ensuri","authors_text":"Abhishek Agarwal, Jianhao Peng, Olgica Milenkovic","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-04T14:32:19Z","title":"Online Convex Matrix Factorization with Representative Regions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.02580","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3bc6cfddda2a70a29a8c7ff1dba7c285aa81749193b362c47da409a34780043c","target":"record","created_at":"2026-05-17T23:44:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b66ab85d1dd2753661a442da923034c0aeb4a462f866f3ccd9e41b62d9ee5c43","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-04T14:32:19Z","title_canon_sha256":"d9e955056f0de7cf4621480183234fcd9414611f2a296d810eb932fd42c29c19"},"schema_version":"1.0","source":{"id":"1904.02580","kind":"arxiv","version":2}},"canonical_sha256":"be540cf9bfe27911700bfdd236eda87a8d9073bfa1e5ebda3761b67a0c7fa3a5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be540cf9bfe27911700bfdd236eda87a8d9073bfa1e5ebda3761b67a0c7fa3a5","first_computed_at":"2026-05-17T23:44:47.320926Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:47.320926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i49/em4oh01yO6Gxh71NX7HuvP9lsfE1nUaDv+s5O1xkMGJx/y0u5tvwjj6sFx0CvyojHxXEIdx3ktyx5NYOCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:47.321621Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.02580","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bc6cfddda2a70a29a8c7ff1dba7c285aa81749193b362c47da409a34780043c","sha256:ee46d692038d91aba115665d9ff537e8a5067ad8adc02179309e674621c26cd6"],"state_sha256":"fce3597d612bd76e17102f93a49e613c638f48c920041c96819d72303bbd7583"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0yoUKr0wDk8zlPfUvBh/9YAI8UHNah55c/H4S3zj/r/RY8UnMuszg1pV/LnKYUngqLfFnzuVQRqyIA4wODnAAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T05:06:54.953215Z","bundle_sha256":"5850384e93f9d6e74f121f7d6454f3f865fe66f961e42a89e3258b048f1d7f26"}}