{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CUJQ7ZRRJ4XUVBBH2FUJFQNCQH","short_pith_number":"pith:CUJQ7ZRR","canonical_record":{"source":{"id":"1704.08458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-27T07:24:35Z","cross_cats_sorted":[],"title_canon_sha256":"ebabb14a83ae6a007ac73fef6f627ceb0cb4c8b586b6a0f9819784eabb735d11","abstract_canon_sha256":"197fc0b2fd141d841b3a8c5b2f5ecb3e6e847d69eb7b583730b6a91466adfcea"},"schema_version":"1.0"},"canonical_sha256":"15130fe6314f2f4a8427d16892c1a281e27f14e9702f5c9284d4d865690c4aee","source":{"kind":"arxiv","id":"1704.08458","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.08458","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"arxiv_version","alias_value":"1704.08458v1","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.08458","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"pith_short_12","alias_value":"CUJQ7ZRRJ4XU","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CUJQ7ZRRJ4XUVBBH","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CUJQ7ZRR","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CUJQ7ZRRJ4XUVBBH2FUJFQNCQH","target":"record","payload":{"canonical_record":{"source":{"id":"1704.08458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-27T07:24:35Z","cross_cats_sorted":[],"title_canon_sha256":"ebabb14a83ae6a007ac73fef6f627ceb0cb4c8b586b6a0f9819784eabb735d11","abstract_canon_sha256":"197fc0b2fd141d841b3a8c5b2f5ecb3e6e847d69eb7b583730b6a91466adfcea"},"schema_version":"1.0"},"canonical_sha256":"15130fe6314f2f4a8427d16892c1a281e27f14e9702f5c9284d4d865690c4aee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:27.646927Z","signature_b64":"fFCDnbZwCbIOo38baNPzJ4DeIvKEECyKAbw8LR5BOreT02rhPLJ33d4h6F9J6gf3FHqHxH/qgW5y891wXFLqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15130fe6314f2f4a8427d16892c1a281e27f14e9702f5c9284d4d865690c4aee","last_reissued_at":"2026-05-18T00:45:27.646567Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:27.646567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.08458","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:45:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zg6cvn8VHly19z9iPp3aD+pGIPwRn36LKyKsDVfWDJMvS7nSgQdqm53rJ9JJPAgqSs7rRKzytVErUr4m05U8AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:43:26.032758Z"},"content_sha256":"93421cdcfce37852b4d33bb625fe1d9eb9e4c900592dd15b46f404c96ecd2098","schema_version":"1.0","event_id":"sha256:93421cdcfce37852b4d33bb625fe1d9eb9e4c900592dd15b46f404c96ecd2098"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CUJQ7ZRRJ4XUVBBH2FUJFQNCQH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Locality Preserving Projections for Grassmann manifold","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baocai Yin, Boyue Wang, Haoran Chen, Junbin Gao, Yanfeng Sun, Yongli Hu","submitted_at":"2017-04-27T07:24:35Z","abstract_excerpt":"Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.08458","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:45:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NSI88V0mNaCixXliY8DkobmS96xlIUxAxxPo+CSOmZ9w88g61hOtzR0hdO5Sqk34rM6GMHZSF4v3uJj0BDDEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:43:26.033105Z"},"content_sha256":"c4a31ec989c96f11d10b26c64cdf1b630dd98d723d458605b8045e627b35bce1","schema_version":"1.0","event_id":"sha256:c4a31ec989c96f11d10b26c64cdf1b630dd98d723d458605b8045e627b35bce1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/bundle.json","state_url":"https://pith.science/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/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":"2026-06-03T01:43:26Z","links":{"resolver":"https://pith.science/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH","bundle":"https://pith.science/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/bundle.json","state":"https://pith.science/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CUJQ7ZRRJ4XUVBBH2FUJFQNCQH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CUJQ7ZRRJ4XUVBBH2FUJFQNCQH","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":"197fc0b2fd141d841b3a8c5b2f5ecb3e6e847d69eb7b583730b6a91466adfcea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-27T07:24:35Z","title_canon_sha256":"ebabb14a83ae6a007ac73fef6f627ceb0cb4c8b586b6a0f9819784eabb735d11"},"schema_version":"1.0","source":{"id":"1704.08458","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.08458","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"arxiv_version","alias_value":"1704.08458v1","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.08458","created_at":"2026-05-18T00:45:27Z"},{"alias_kind":"pith_short_12","alias_value":"CUJQ7ZRRJ4XU","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CUJQ7ZRRJ4XUVBBH","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CUJQ7ZRR","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:c4a31ec989c96f11d10b26c64cdf1b630dd98d723d458605b8045e627b35bce1","target":"graph","created_at":"2026-05-18T00:45:27Z","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":"Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used d","authors_text":"Baocai Yin, Boyue Wang, Haoran Chen, Junbin Gao, Yanfeng Sun, Yongli Hu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-27T07:24:35Z","title":"Locality Preserving Projections for Grassmann manifold"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.08458","kind":"arxiv","version":1},"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":"sha256:93421cdcfce37852b4d33bb625fe1d9eb9e4c900592dd15b46f404c96ecd2098","target":"record","created_at":"2026-05-18T00:45:27Z","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":"197fc0b2fd141d841b3a8c5b2f5ecb3e6e847d69eb7b583730b6a91466adfcea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-27T07:24:35Z","title_canon_sha256":"ebabb14a83ae6a007ac73fef6f627ceb0cb4c8b586b6a0f9819784eabb735d11"},"schema_version":"1.0","source":{"id":"1704.08458","kind":"arxiv","version":1}},"canonical_sha256":"15130fe6314f2f4a8427d16892c1a281e27f14e9702f5c9284d4d865690c4aee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15130fe6314f2f4a8427d16892c1a281e27f14e9702f5c9284d4d865690c4aee","first_computed_at":"2026-05-18T00:45:27.646567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:27.646567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fFCDnbZwCbIOo38baNPzJ4DeIvKEECyKAbw8LR5BOreT02rhPLJ33d4h6F9J6gf3FHqHxH/qgW5y891wXFLqBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:27.646927Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.08458","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93421cdcfce37852b4d33bb625fe1d9eb9e4c900592dd15b46f404c96ecd2098","sha256:c4a31ec989c96f11d10b26c64cdf1b630dd98d723d458605b8045e627b35bce1"],"state_sha256":"566719ccb8138cb650d97a5e3c051b3a3e6b00d1805a514bf6201190264e07fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VT0nTICRQ7g7WJXtEhy2/0IdL6I2oSnJXkVrZC6gHHQ7mtoKc8ngjK/qShvAyv1Bb5CftVGd5CxAXTS/QaLtAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T01:43:26.036048Z","bundle_sha256":"5a6696a3798d4d43a115984c3175fbaa7cbbf73b8e91df9407a10c167e4684f1"}}