{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2P47DABRZCNRNJIVVX2OEKQTQ3","short_pith_number":"pith:2P47DABR","schema_version":"1.0","canonical_sha256":"d3f9f18031c89b16a515adf4e22a1386d907b5589800964d24b0257f8ebed72c","source":{"kind":"arxiv","id":"1807.10614","version":1},"attestation_state":"computed","paper":{"title":"Multi-view Reconstructive Preserving Embedding for Dimension Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adong Kong, Bo Jin, Huibing Wang, Lin Feng","submitted_at":"2018-07-25T06:42:58Z","abstract_excerpt":"With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can re ect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reducti"},"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":"1807.10614","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-25T06:42:58Z","cross_cats_sorted":[],"title_canon_sha256":"d8f2ec3a29556df3c1a1d92bcb7e203d80312e55cfe574d77a89458938774978","abstract_canon_sha256":"12ab17d957a8cd1ce7426e240f08801ee8da49fa17c80142ed7eb8ad3113d113"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:40.012306Z","signature_b64":"WbQiWq9veDS+zk2IYiXHEbWnuA0PNrVinIvGeVkEVQGY0/FR3I6JCu3jsvvfP/1nZbGe2iZ4DLjyOLndDVNhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3f9f18031c89b16a515adf4e22a1386d907b5589800964d24b0257f8ebed72c","last_reissued_at":"2026-05-18T00:09:40.011715Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:40.011715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-view Reconstructive Preserving Embedding for Dimension Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adong Kong, Bo Jin, Huibing Wang, Lin Feng","submitted_at":"2018-07-25T06:42:58Z","abstract_excerpt":"With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can re ect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reducti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10614","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":"1807.10614","created_at":"2026-05-18T00:09:40.011800+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.10614v1","created_at":"2026-05-18T00:09:40.011800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10614","created_at":"2026-05-18T00:09:40.011800+00:00"},{"alias_kind":"pith_short_12","alias_value":"2P47DABRZCNR","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2P47DABRZCNRNJIV","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2P47DABR","created_at":"2026-05-18T12:32:02.567920+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/2P47DABRZCNRNJIVVX2OEKQTQ3","json":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3.json","graph_json":"https://pith.science/api/pith-number/2P47DABRZCNRNJIVVX2OEKQTQ3/graph.json","events_json":"https://pith.science/api/pith-number/2P47DABRZCNRNJIVVX2OEKQTQ3/events.json","paper":"https://pith.science/paper/2P47DABR"},"agent_actions":{"view_html":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3","download_json":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3.json","view_paper":"https://pith.science/paper/2P47DABR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.10614&json=true","fetch_graph":"https://pith.science/api/pith-number/2P47DABRZCNRNJIVVX2OEKQTQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/2P47DABRZCNRNJIVVX2OEKQTQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3/action/storage_attestation","attest_author":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3/action/author_attestation","sign_citation":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3/action/citation_signature","submit_replication":"https://pith.science/pith/2P47DABRZCNRNJIVVX2OEKQTQ3/action/replication_record"}},"created_at":"2026-05-18T00:09:40.011800+00:00","updated_at":"2026-05-18T00:09:40.011800+00:00"}