{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:KYPHV3B7FQL3QRP5NJFFUGWKKD","short_pith_number":"pith:KYPHV3B7","schema_version":"1.0","canonical_sha256":"561e7aec3f2c17b845fd6a4a5a1aca50f9ab95f5d63df75aa78e422b4ab1a1d2","source":{"kind":"arxiv","id":"1303.3664","version":2},"attestation_state":"computed","paper":{"title":"Topic Discovery through Data Dependent and Random Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama, Weicong Ding","submitted_at":"2013-03-15T02:37:19Z","abstract_excerpt":"We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms based on data-dependent and random projections of word-frequency patterns to identify novel words and associated topics. We will also discuss the statistical guarantees of the data-dependent projections method based on two mild assumptions on the prior density of topic document matrix"},"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":"1303.3664","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-03-15T02:37:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3e979a3463f3b9ae9e8c7d7a9dc87007fc3c2610b48b2a5d62a9267e4495dcff","abstract_canon_sha256":"e3464c13d982b749092acfb7a5498e3ecd25e91133976aafd79a80d01547894c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:30:40.646376Z","signature_b64":"943lEba2v10PKxe4mkAL1iys4Jq2CPSpWiaKqaO1196TJ89blCR9WpuZ0ru+4rbmpomS5iM5aEhNTIujPNG/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"561e7aec3f2c17b845fd6a4a5a1aca50f9ab95f5d63df75aa78e422b4ab1a1d2","last_reissued_at":"2026-05-18T03:30:40.645289Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:30:40.645289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Topic Discovery through Data Dependent and Random Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama, Weicong Ding","submitted_at":"2013-03-15T02:37:19Z","abstract_excerpt":"We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms based on data-dependent and random projections of word-frequency patterns to identify novel words and associated topics. We will also discuss the statistical guarantees of the data-dependent projections method based on two mild assumptions on the prior density of topic document matrix"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.3664","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":"1303.3664","created_at":"2026-05-18T03:30:40.645513+00:00"},{"alias_kind":"arxiv_version","alias_value":"1303.3664v2","created_at":"2026-05-18T03:30:40.645513+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.3664","created_at":"2026-05-18T03:30:40.645513+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYPHV3B7FQL3","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYPHV3B7FQL3QRP5","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYPHV3B7","created_at":"2026-05-18T12:27:51.066281+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/KYPHV3B7FQL3QRP5NJFFUGWKKD","json":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD.json","graph_json":"https://pith.science/api/pith-number/KYPHV3B7FQL3QRP5NJFFUGWKKD/graph.json","events_json":"https://pith.science/api/pith-number/KYPHV3B7FQL3QRP5NJFFUGWKKD/events.json","paper":"https://pith.science/paper/KYPHV3B7"},"agent_actions":{"view_html":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD","download_json":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD.json","view_paper":"https://pith.science/paper/KYPHV3B7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1303.3664&json=true","fetch_graph":"https://pith.science/api/pith-number/KYPHV3B7FQL3QRP5NJFFUGWKKD/graph.json","fetch_events":"https://pith.science/api/pith-number/KYPHV3B7FQL3QRP5NJFFUGWKKD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD/action/storage_attestation","attest_author":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD/action/author_attestation","sign_citation":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD/action/citation_signature","submit_replication":"https://pith.science/pith/KYPHV3B7FQL3QRP5NJFFUGWKKD/action/replication_record"}},"created_at":"2026-05-18T03:30:40.645513+00:00","updated_at":"2026-05-18T03:30:40.645513+00:00"}