{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LKLWHKXOD2FPXP233SZ4H3ELYB","short_pith_number":"pith:LKLWHKXO","schema_version":"1.0","canonical_sha256":"5a9763aaee1e8afbbf5bdcb3c3ec8bc0738c06f791907d189440a5704ea9a351","source":{"kind":"arxiv","id":"1602.00370","version":2},"attestation_state":"computed","paper":{"title":"Visualizing Large-scale and High-dimensional Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei","submitted_at":"2016-02-01T03:01:33Z","abstract_excerpt":"We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-of-the-art methods such as the t-SNE from scaling to large-scale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first"},"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":"1602.00370","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2016-02-01T03:01:33Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"3666da3d87ff8c5c5c561676f11c987e49dfab1f792f807964fd6630126b0943","abstract_canon_sha256":"c1e0cb9214c98e68db57fc5f84cca087478eccc021a0499e7cdbdc9d16db5e28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:46.831704Z","signature_b64":"+jS2JPkOyU4NwEUs56DzT+/gPUGIr5QFEJPK5/hJYlkOkM1Kf1FlzrFqNEqaRWcG2wpcVpZ3VQ3FRhaeYGRmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a9763aaee1e8afbbf5bdcb3c3ec8bc0738c06f791907d189440a5704ea9a351","last_reissued_at":"2026-05-18T01:17:46.831008Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:46.831008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Visualizing Large-scale and High-dimensional Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei","submitted_at":"2016-02-01T03:01:33Z","abstract_excerpt":"We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-of-the-art methods such as the t-SNE from scaling to large-scale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.00370","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":"1602.00370","created_at":"2026-05-18T01:17:46.831102+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.00370v2","created_at":"2026-05-18T01:17:46.831102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.00370","created_at":"2026-05-18T01:17:46.831102+00:00"},{"alias_kind":"pith_short_12","alias_value":"LKLWHKXOD2FP","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"LKLWHKXOD2FPXP23","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"LKLWHKXO","created_at":"2026-05-18T12:30:29.479603+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/LKLWHKXOD2FPXP233SZ4H3ELYB","json":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB.json","graph_json":"https://pith.science/api/pith-number/LKLWHKXOD2FPXP233SZ4H3ELYB/graph.json","events_json":"https://pith.science/api/pith-number/LKLWHKXOD2FPXP233SZ4H3ELYB/events.json","paper":"https://pith.science/paper/LKLWHKXO"},"agent_actions":{"view_html":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB","download_json":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB.json","view_paper":"https://pith.science/paper/LKLWHKXO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.00370&json=true","fetch_graph":"https://pith.science/api/pith-number/LKLWHKXOD2FPXP233SZ4H3ELYB/graph.json","fetch_events":"https://pith.science/api/pith-number/LKLWHKXOD2FPXP233SZ4H3ELYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB/action/storage_attestation","attest_author":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB/action/author_attestation","sign_citation":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB/action/citation_signature","submit_replication":"https://pith.science/pith/LKLWHKXOD2FPXP233SZ4H3ELYB/action/replication_record"}},"created_at":"2026-05-18T01:17:46.831102+00:00","updated_at":"2026-05-18T01:17:46.831102+00:00"}