{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:56LBRDKG6WLGQTYXGBSLFC6OGC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f7f3db9dff2f691a189fc2bd44b50018d7f785eb40614b5b2c79a99e1f85060d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-27T23:37:20Z","title_canon_sha256":"dd8c9d6ef8e26a99938562a6a75b91d7a03b2d294c5beaf5d8257c056ac48476"},"schema_version":"1.0","source":{"id":"2111.14007","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.14007","created_at":"2026-07-05T03:35:38Z"},{"alias_kind":"arxiv_version","alias_value":"2111.14007v1","created_at":"2026-07-05T03:35:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.14007","created_at":"2026-07-05T03:35:38Z"},{"alias_kind":"pith_short_12","alias_value":"56LBRDKG6WLG","created_at":"2026-07-05T03:35:38Z"},{"alias_kind":"pith_short_16","alias_value":"56LBRDKG6WLGQTYX","created_at":"2026-07-05T03:35:38Z"},{"alias_kind":"pith_short_8","alias_value":"56LBRDKG","created_at":"2026-07-05T03:35:38Z"}],"graph_snapshots":[{"event_id":"sha256:2f5715db9251b1b5ca0071cddeb74e25ed21a70637276bb092b1ca10c7d882f8","target":"graph","created_at":"2026-07-05T03:35:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2111.14007/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representation. For example, in a human-face data set, if an image contains a hat on the head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorizing. This paper proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize thei","authors_text":"Bingxue Wu, Can Tong, Jiao Wei, Qiang He, Shouliang Qi, Yudong Yao, YueYang Teng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-27T23:37:20Z","title":"An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.14007","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a3e02fa45f8097dc36ddcd98ee4c68a55e0a7d0fc8d1c372f8336c586579b122","target":"record","created_at":"2026-07-05T03:35:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f7f3db9dff2f691a189fc2bd44b50018d7f785eb40614b5b2c79a99e1f85060d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-27T23:37:20Z","title_canon_sha256":"dd8c9d6ef8e26a99938562a6a75b91d7a03b2d294c5beaf5d8257c056ac48476"},"schema_version":"1.0","source":{"id":"2111.14007","kind":"arxiv","version":1}},"canonical_sha256":"ef96188d46f596684f173064b28bce30a731f73521b6e2882d9d3a54c8532b4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef96188d46f596684f173064b28bce30a731f73521b6e2882d9d3a54c8532b4d","first_computed_at":"2026-07-05T03:35:38.113960Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:35:38.113960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tg2vDizRwseOP7hCThtuG/Plc8txl7EY1cy5AIJCJIGKD8orfic1e1ipFhrB70gFC/qrYDdPPHIg3zzyOp8uAA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:35:38.114446Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.14007","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3e02fa45f8097dc36ddcd98ee4c68a55e0a7d0fc8d1c372f8336c586579b122","sha256:2f5715db9251b1b5ca0071cddeb74e25ed21a70637276bb092b1ca10c7d882f8"],"state_sha256":"79a9717ac3db5e587a5b04648f3379a967f5b8264ed79a02c9712246460c16c8"}