{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:UYYGJTXREM743FHDA7NKDZHEDZ","short_pith_number":"pith:UYYGJTXR","schema_version":"1.0","canonical_sha256":"a63064cef1233fcd94e307daa1e4e41e690025a189627cdd75b22eaf06f98a8c","source":{"kind":"arxiv","id":"2006.10520","version":1},"attestation_state":"computed","paper":{"title":"Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huibing Wang, Lin Feng, Xiangzhu Meng","submitted_at":"2020-06-14T12:47:25Z","abstract_excerpt":"In recent years, we have witnessed a surge of interest in multi-view representation learning, which is concerned with the problem of learning representations of multi-view data. When facing multiple views that are highly related but sightly different from each other, most of existing multi-view methods might fail to fully integrate multi-view information. Besides, correlations between features from multiple views always vary seriously, which makes multi-view representation challenging. Therefore, how to learn appropriate embedding from multi-view information is still an open problem but challe"},"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":"2006.10520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-14T12:47:25Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"595d716e6f584312a23ee9cc41df438a8a58c5bb9d0f0b4b7f8ee44e52c38124","abstract_canon_sha256":"000721b1f8df1be2e6957d10f782bbd99476ab844a5c0b3c6e8744a822098376"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:21.448419Z","signature_b64":"o9mjFjvFkmhBnzwjCQMklYs1g7xbkVbQtzftw5rNrvZl3xr2Q1OVejCq7zLufEc5KlmX+77ElSdPuE1ENp0kBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a63064cef1233fcd94e307daa1e4e41e690025a189627cdd75b22eaf06f98a8c","last_reissued_at":"2026-07-05T01:11:21.448011Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:21.448011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huibing Wang, Lin Feng, Xiangzhu Meng","submitted_at":"2020-06-14T12:47:25Z","abstract_excerpt":"In recent years, we have witnessed a surge of interest in multi-view representation learning, which is concerned with the problem of learning representations of multi-view data. When facing multiple views that are highly related but sightly different from each other, most of existing multi-view methods might fail to fully integrate multi-view information. Besides, correlations between features from multiple views always vary seriously, which makes multi-view representation challenging. Therefore, how to learn appropriate embedding from multi-view information is still an open problem but challe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10520","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.10520/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":"2006.10520","created_at":"2026-07-05T01:11:21.448067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.10520v1","created_at":"2026-07-05T01:11:21.448067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10520","created_at":"2026-07-05T01:11:21.448067+00:00"},{"alias_kind":"pith_short_12","alias_value":"UYYGJTXREM74","created_at":"2026-07-05T01:11:21.448067+00:00"},{"alias_kind":"pith_short_16","alias_value":"UYYGJTXREM743FHD","created_at":"2026-07-05T01:11:21.448067+00:00"},{"alias_kind":"pith_short_8","alias_value":"UYYGJTXR","created_at":"2026-07-05T01:11:21.448067+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/UYYGJTXREM743FHDA7NKDZHEDZ","json":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ.json","graph_json":"https://pith.science/api/pith-number/UYYGJTXREM743FHDA7NKDZHEDZ/graph.json","events_json":"https://pith.science/api/pith-number/UYYGJTXREM743FHDA7NKDZHEDZ/events.json","paper":"https://pith.science/paper/UYYGJTXR"},"agent_actions":{"view_html":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ","download_json":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ.json","view_paper":"https://pith.science/paper/UYYGJTXR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.10520&json=true","fetch_graph":"https://pith.science/api/pith-number/UYYGJTXREM743FHDA7NKDZHEDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/UYYGJTXREM743FHDA7NKDZHEDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ/action/storage_attestation","attest_author":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ/action/author_attestation","sign_citation":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ/action/citation_signature","submit_replication":"https://pith.science/pith/UYYGJTXREM743FHDA7NKDZHEDZ/action/replication_record"}},"created_at":"2026-07-05T01:11:21.448067+00:00","updated_at":"2026-07-05T01:11:21.448067+00:00"}