{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:AX3UL6KHBDEK3HHR5TGLP3Q6S2","short_pith_number":"pith:AX3UL6KH","schema_version":"1.0","canonical_sha256":"05f745f94708c8ad9cf1ecccb7ee1e96b54ab4dccb020982403aa4a5e03e258d","source":{"kind":"arxiv","id":"1507.08173","version":2},"attestation_state":"computed","paper":{"title":"Fast Robust PCA on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gilles Puy, Nathanael Perraudin, Nauman Shahid, Pierre Vandergheynst, Vassilis Kalofolias","submitted_at":"2015-07-29T14:53:33Z","abstract_excerpt":"Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) and susceptibility to gross corruptions in the data. In this paper we propose a principal component analysis "},"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":"1507.08173","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-07-29T14:53:33Z","cross_cats_sorted":[],"title_canon_sha256":"f98a1dcd797102bbf45845b5f33d8ddf43b6ad09d1b3e9b7a8aa23b45a409b1c","abstract_canon_sha256":"167171a1ad630db24efcbf4ced6617afe951bae25ac370fa3d3c5f5c2f11ccd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:03.757153Z","signature_b64":"5FIgCAt1MDmzHSxPvA+yplB/Y6WpMoZnCNkNFx5BZ3OvkXOTpnnNN3KWZIu4YXFMmln5hkBsmFVhuHawfSa5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05f745f94708c8ad9cf1ecccb7ee1e96b54ab4dccb020982403aa4a5e03e258d","last_reissued_at":"2026-05-18T01:14:03.756595Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:03.756595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Robust PCA on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gilles Puy, Nathanael Perraudin, Nauman Shahid, Pierre Vandergheynst, Vassilis Kalofolias","submitted_at":"2015-07-29T14:53:33Z","abstract_excerpt":"Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) and susceptibility to gross corruptions in the data. In this paper we propose a principal component analysis "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.08173","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1507.08173","created_at":"2026-05-18T01:14:03.756672+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.08173v2","created_at":"2026-05-18T01:14:03.756672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.08173","created_at":"2026-05-18T01:14:03.756672+00:00"},{"alias_kind":"pith_short_12","alias_value":"AX3UL6KHBDEK","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"AX3UL6KHBDEK3HHR","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"AX3UL6KH","created_at":"2026-05-18T12:29:10.953037+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/AX3UL6KHBDEK3HHR5TGLP3Q6S2","json":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2.json","graph_json":"https://pith.science/api/pith-number/AX3UL6KHBDEK3HHR5TGLP3Q6S2/graph.json","events_json":"https://pith.science/api/pith-number/AX3UL6KHBDEK3HHR5TGLP3Q6S2/events.json","paper":"https://pith.science/paper/AX3UL6KH"},"agent_actions":{"view_html":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2","download_json":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2.json","view_paper":"https://pith.science/paper/AX3UL6KH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.08173&json=true","fetch_graph":"https://pith.science/api/pith-number/AX3UL6KHBDEK3HHR5TGLP3Q6S2/graph.json","fetch_events":"https://pith.science/api/pith-number/AX3UL6KHBDEK3HHR5TGLP3Q6S2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2/action/storage_attestation","attest_author":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2/action/author_attestation","sign_citation":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2/action/citation_signature","submit_replication":"https://pith.science/pith/AX3UL6KHBDEK3HHR5TGLP3Q6S2/action/replication_record"}},"created_at":"2026-05-18T01:14:03.756672+00:00","updated_at":"2026-05-18T01:14:03.756672+00:00"}