{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:HPP3U23SDQAP2EBWHFC4RGLBZW","short_pith_number":"pith:HPP3U23S","schema_version":"1.0","canonical_sha256":"3bdfba6b721c00fd10363945c89961cdadcea00ee5339a0c9400680959affab5","source":{"kind":"arxiv","id":"1608.04689","version":1},"attestation_state":"computed","paper":{"title":"A Shallow High-Order Parametric Approach to Data Visualization and Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Dongjin Song, Hongyu Guo, Martin Renqiang Min","submitted_at":"2016-08-16T17:54:40Z","abstract_excerpt":"Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We present a supervised discriminative High-Order Parametric Embedding (HOPE) approach to data visualization and compression. Compared to deep embedding models with complicated deep architectures, HOPE generates more effective high-order feature mapping through an embarrassingly simple shallow model. Furthermore, two approaches to generating a small number of exemplars conveying high-order interactions to represent large-s"},"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":"1608.04689","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-08-16T17:54:40Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"92f6040f3cc7272deeb73a742cbd9237c0dbdf3cc20e7641d09bbf0e4c2d1c1c","abstract_canon_sha256":"b06433ecda3216b522f69cd7a4bbcd1eed3b90b168beec12ca925082ec456e3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:36.528242Z","signature_b64":"LYMgKQh6NWD5uuTvKnOs/KCQd5Baci+zRCUKDgWRXN5i2TFyVp74gK2knLZHtDY/n5TDYNaZ4BN4kbWNg0t4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bdfba6b721c00fd10363945c89961cdadcea00ee5339a0c9400680959affab5","last_reissued_at":"2026-05-18T01:08:36.527665Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:36.527665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Shallow High-Order Parametric Approach to Data Visualization and Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Dongjin Song, Hongyu Guo, Martin Renqiang Min","submitted_at":"2016-08-16T17:54:40Z","abstract_excerpt":"Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We present a supervised discriminative High-Order Parametric Embedding (HOPE) approach to data visualization and compression. Compared to deep embedding models with complicated deep architectures, HOPE generates more effective high-order feature mapping through an embarrassingly simple shallow model. Furthermore, two approaches to generating a small number of exemplars conveying high-order interactions to represent large-s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04689","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":"1608.04689","created_at":"2026-05-18T01:08:36.527724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.04689v1","created_at":"2026-05-18T01:08:36.527724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04689","created_at":"2026-05-18T01:08:36.527724+00:00"},{"alias_kind":"pith_short_12","alias_value":"HPP3U23SDQAP","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"HPP3U23SDQAP2EBW","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"HPP3U23S","created_at":"2026-05-18T12:30:19.053100+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/HPP3U23SDQAP2EBWHFC4RGLBZW","json":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW.json","graph_json":"https://pith.science/api/pith-number/HPP3U23SDQAP2EBWHFC4RGLBZW/graph.json","events_json":"https://pith.science/api/pith-number/HPP3U23SDQAP2EBWHFC4RGLBZW/events.json","paper":"https://pith.science/paper/HPP3U23S"},"agent_actions":{"view_html":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW","download_json":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW.json","view_paper":"https://pith.science/paper/HPP3U23S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.04689&json=true","fetch_graph":"https://pith.science/api/pith-number/HPP3U23SDQAP2EBWHFC4RGLBZW/graph.json","fetch_events":"https://pith.science/api/pith-number/HPP3U23SDQAP2EBWHFC4RGLBZW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW/action/storage_attestation","attest_author":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW/action/author_attestation","sign_citation":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW/action/citation_signature","submit_replication":"https://pith.science/pith/HPP3U23SDQAP2EBWHFC4RGLBZW/action/replication_record"}},"created_at":"2026-05-18T01:08:36.527724+00:00","updated_at":"2026-05-18T01:08:36.527724+00:00"}