{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MT6E2FCVLQIBK2YETLLUJAVDFH","short_pith_number":"pith:MT6E2FCV","schema_version":"1.0","canonical_sha256":"64fc4d14555c10156b049ad74482a329efa0b74b69b733865fe8685798bef5a4","source":{"kind":"arxiv","id":"1802.05756","version":1},"attestation_state":"computed","paper":{"title":"Inferring relevant features: from QFT to PCA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph","stat.ML"],"primary_cat":"cs.LG","authors_text":"C\\'edric B\\'eny","submitted_at":"2018-02-16T06:24:04Z","abstract_excerpt":"In many-body physics, renormalization techniques are used to extract aspects of a statistical or quantum state that are relevant at large scale, or for low energy experiments. Recent works have proposed that these features can be formally identified as those perturbations of the states whose distinguishability most resist coarse-graining. Here, we examine whether this same strategy can be used to identify important features of an unlabeled dataset. This approach indeed results in a technique very similar to kernel PCA (principal component analysis), but with a kernel function that is automatic"},"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":"1802.05756","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-16T06:24:04Z","cross_cats_sorted":["quant-ph","stat.ML"],"title_canon_sha256":"6baaf41f6d2774d4597a39d44a0e0a25ae516d5d59fc8b71ec1e113e3a78432e","abstract_canon_sha256":"ca72f53b95646b35e925957aed9309cb12d7fd7f7b6f09c20820fb7f653078e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:59.551760Z","signature_b64":"UA6Bn1j4iJaJpEVmQEHo6AkikEWHlH+KaQbIOJtZp4HJJNsH7mUAh4y1A/L38IMfkDsq4Dz06oLbkDqxl8SRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64fc4d14555c10156b049ad74482a329efa0b74b69b733865fe8685798bef5a4","last_reissued_at":"2026-05-17T23:57:59.551118Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:59.551118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring relevant features: from QFT to PCA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["quant-ph","stat.ML"],"primary_cat":"cs.LG","authors_text":"C\\'edric B\\'eny","submitted_at":"2018-02-16T06:24:04Z","abstract_excerpt":"In many-body physics, renormalization techniques are used to extract aspects of a statistical or quantum state that are relevant at large scale, or for low energy experiments. Recent works have proposed that these features can be formally identified as those perturbations of the states whose distinguishability most resist coarse-graining. Here, we examine whether this same strategy can be used to identify important features of an unlabeled dataset. This approach indeed results in a technique very similar to kernel PCA (principal component analysis), but with a kernel function that is automatic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05756","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":"1802.05756","created_at":"2026-05-17T23:57:59.551221+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.05756v1","created_at":"2026-05-17T23:57:59.551221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05756","created_at":"2026-05-17T23:57:59.551221+00:00"},{"alias_kind":"pith_short_12","alias_value":"MT6E2FCVLQIB","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"MT6E2FCVLQIBK2YE","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"MT6E2FCV","created_at":"2026-05-18T12:32:40.477152+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/MT6E2FCVLQIBK2YETLLUJAVDFH","json":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH.json","graph_json":"https://pith.science/api/pith-number/MT6E2FCVLQIBK2YETLLUJAVDFH/graph.json","events_json":"https://pith.science/api/pith-number/MT6E2FCVLQIBK2YETLLUJAVDFH/events.json","paper":"https://pith.science/paper/MT6E2FCV"},"agent_actions":{"view_html":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH","download_json":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH.json","view_paper":"https://pith.science/paper/MT6E2FCV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.05756&json=true","fetch_graph":"https://pith.science/api/pith-number/MT6E2FCVLQIBK2YETLLUJAVDFH/graph.json","fetch_events":"https://pith.science/api/pith-number/MT6E2FCVLQIBK2YETLLUJAVDFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH/action/storage_attestation","attest_author":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH/action/author_attestation","sign_citation":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH/action/citation_signature","submit_replication":"https://pith.science/pith/MT6E2FCVLQIBK2YETLLUJAVDFH/action/replication_record"}},"created_at":"2026-05-17T23:57:59.551221+00:00","updated_at":"2026-05-17T23:57:59.551221+00:00"}