{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZV562JTHI3V5MEL6UM6OLDVEYG","short_pith_number":"pith:ZV562JTH","canonical_record":{"source":{"id":"2501.00726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-01-01T05:05:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"efbb0c88a4bcd9923b88325ab1e101bce5af69737efc21f95d9a9d3d7d54e765","abstract_canon_sha256":"7ee37ceb21bcaddeb4af87d3bd29573f6a9888c3a7c35786690f642deea65ee0"},"schema_version":"1.0"},"canonical_sha256":"cd7bed266746ebd6117ea33ce58ea4c1aa8519ff9b958e7e878aa719c8863e31","source":{"kind":"arxiv","id":"2501.00726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00726","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00726v1","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00726","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_12","alias_value":"ZV562JTHI3V5","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_16","alias_value":"ZV562JTHI3V5MEL6","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_8","alias_value":"ZV562JTH","created_at":"2026-07-05T09:56:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZV562JTHI3V5MEL6UM6OLDVEYG","target":"record","payload":{"canonical_record":{"source":{"id":"2501.00726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-01-01T05:05:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"efbb0c88a4bcd9923b88325ab1e101bce5af69737efc21f95d9a9d3d7d54e765","abstract_canon_sha256":"7ee37ceb21bcaddeb4af87d3bd29573f6a9888c3a7c35786690f642deea65ee0"},"schema_version":"1.0"},"canonical_sha256":"cd7bed266746ebd6117ea33ce58ea4c1aa8519ff9b958e7e878aa719c8863e31","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:09.455501Z","signature_b64":"abf4ulnopZwbgU+lgdn3lIi3xfwuZ4ZiSvB1oPPn5Aswplyjw5pR/WVL5ovwwBU5efpo1ttPswkaHEvewBhpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd7bed266746ebd6117ea33ce58ea4c1aa8519ff9b958e7e878aa719c8863e31","last_reissued_at":"2026-07-05T09:56:09.454943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:09.454943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.00726","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hALG6JIKg1mQuTFDTbzoT5gc723cW/AfikJzGGcSlAWyaq0tU46Co1Gat5PPWOWtHEOr7kNkqEAb38IL5gttDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:30:33.704189Z"},"content_sha256":"6da92b5c7219f3675053a958e32ccec113f0f10a54f6640cc3406be700464faa","schema_version":"1.0","event_id":"sha256:6da92b5c7219f3675053a958e32ccec113f0f10a54f6640cc3406be700464faa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZV562JTHI3V5MEL6UM6OLDVEYG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Anning Yang, Chenyi Huang, Wanquan Liu, Xianchao Xiu, Xinrong Li","submitted_at":"2025-01-01T05:05:46Z","abstract_excerpt":"Unsupervised feature selection (UFS) is widely applied in machine learning and pattern recognition. However, most of the existing methods only consider a single sparsity, which makes it difficult to select valuable and discriminative feature subsets from the original high-dimensional feature set. In this paper, we propose a new UFS method called DSCOFS via embedding double sparsity constrained optimization into the classical principal component analysis (PCA) framework. Double sparsity refers to using $\\ell_{2,0}$-norm and $\\ell_0$-norm to simultaneously constrain variables, by adding the spar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00726","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.00726/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HO19LzF64G8eZl9XRet/gAtikWynAVep622YJen5qiQ11lty7b6u3dz9vIcHTRm6mSLbmHBVfCkA6qCdHNSzCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:30:33.705124Z"},"content_sha256":"ce561347da125b849064a4013fe6ccfadd96537164255c663255a73f30449633","schema_version":"1.0","event_id":"sha256:ce561347da125b849064a4013fe6ccfadd96537164255c663255a73f30449633"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/bundle.json","state_url":"https://pith.science/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T00:30:33Z","links":{"resolver":"https://pith.science/pith/ZV562JTHI3V5MEL6UM6OLDVEYG","bundle":"https://pith.science/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/bundle.json","state":"https://pith.science/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZV562JTHI3V5MEL6UM6OLDVEYG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZV562JTHI3V5MEL6UM6OLDVEYG","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":"7ee37ceb21bcaddeb4af87d3bd29573f6a9888c3a7c35786690f642deea65ee0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-01-01T05:05:46Z","title_canon_sha256":"efbb0c88a4bcd9923b88325ab1e101bce5af69737efc21f95d9a9d3d7d54e765"},"schema_version":"1.0","source":{"id":"2501.00726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00726","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00726v1","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00726","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_12","alias_value":"ZV562JTHI3V5","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_16","alias_value":"ZV562JTHI3V5MEL6","created_at":"2026-07-05T09:56:09Z"},{"alias_kind":"pith_short_8","alias_value":"ZV562JTH","created_at":"2026-07-05T09:56:09Z"}],"graph_snapshots":[{"event_id":"sha256:ce561347da125b849064a4013fe6ccfadd96537164255c663255a73f30449633","target":"graph","created_at":"2026-07-05T09:56:09Z","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/2501.00726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised feature selection (UFS) is widely applied in machine learning and pattern recognition. However, most of the existing methods only consider a single sparsity, which makes it difficult to select valuable and discriminative feature subsets from the original high-dimensional feature set. In this paper, we propose a new UFS method called DSCOFS via embedding double sparsity constrained optimization into the classical principal component analysis (PCA) framework. Double sparsity refers to using $\\ell_{2,0}$-norm and $\\ell_0$-norm to simultaneously constrain variables, by adding the spar","authors_text":"Anning Yang, Chenyi Huang, Wanquan Liu, Xianchao Xiu, Xinrong Li","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-01-01T05:05:46Z","title":"Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00726","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":"sha256:6da92b5c7219f3675053a958e32ccec113f0f10a54f6640cc3406be700464faa","target":"record","created_at":"2026-07-05T09:56:09Z","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":"7ee37ceb21bcaddeb4af87d3bd29573f6a9888c3a7c35786690f642deea65ee0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-01-01T05:05:46Z","title_canon_sha256":"efbb0c88a4bcd9923b88325ab1e101bce5af69737efc21f95d9a9d3d7d54e765"},"schema_version":"1.0","source":{"id":"2501.00726","kind":"arxiv","version":1}},"canonical_sha256":"cd7bed266746ebd6117ea33ce58ea4c1aa8519ff9b958e7e878aa719c8863e31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd7bed266746ebd6117ea33ce58ea4c1aa8519ff9b958e7e878aa719c8863e31","first_computed_at":"2026-07-05T09:56:09.454943Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:09.454943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"abf4ulnopZwbgU+lgdn3lIi3xfwuZ4ZiSvB1oPPn5Aswplyjw5pR/WVL5ovwwBU5efpo1ttPswkaHEvewBhpAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:09.455501Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.00726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6da92b5c7219f3675053a958e32ccec113f0f10a54f6640cc3406be700464faa","sha256:ce561347da125b849064a4013fe6ccfadd96537164255c663255a73f30449633"],"state_sha256":"e57c78128af2663a6dc2c1b37df9f9bded0bf0e9f0fa07deda612337021b1bdf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4KZ7fqXpw/P3gq3fpUF/bPYUKv4PRNad5NQ1TqQe+F3ud3cC3tYGtMBHaJqXIgJnaZtj2+4f8YloplbTHIsyBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T00:30:33.711132Z","bundle_sha256":"f199ef4cd6fb3934db2f03645a2689be076429ed94e2997bc23a957bd4442e82"}}