{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:Z2EGKEBRXPB7KBQ6XT26CMCF4J","short_pith_number":"pith:Z2EGKEBR","schema_version":"1.0","canonical_sha256":"ce88651031bbc3f5061ebcf5e13045e27f94417dd42c6f15b905829fc0f2aaff","source":{"kind":"arxiv","id":"1103.0102","version":2},"attestation_state":"computed","paper":{"title":"Multi-label Learning via Structured Decomposition and Group Sparsity","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dacheng Tao, Tianyi Zhou","submitted_at":"2011-03-01T08:15:28Z","abstract_excerpt":"In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label structure in the feature space is not clear. In this paper, we propose a novel multi-label learning method \"Structured Decomposition + Group Sparsity (SDGS)\". In SDGS, we learn a feature subspace for each label from the structured decomposition of the training data, and predict t"},"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":"1103.0102","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2011-03-01T08:15:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bc7eb0ec679a847658ee8b70ec7b5b9b6f68e8ab32f0a9f5307935550241bba3","abstract_canon_sha256":"420c1b5b12391b7369fcf7677aefcd1d81f77235f83f934510a9f7d0c9e53179"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:27:27.820973Z","signature_b64":"oKe7AjqogSIn+hwxYIia+3x/TU6Kjqt0XFbOFwlWs5unJVnGO9xenj6DPNNAYi4PnEiJg3Shf/ehk7f5kRImCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce88651031bbc3f5061ebcf5e13045e27f94417dd42c6f15b905829fc0f2aaff","last_reissued_at":"2026-05-18T04:27:27.820532Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:27:27.820532Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-label Learning via Structured Decomposition and Group Sparsity","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dacheng Tao, Tianyi Zhou","submitted_at":"2011-03-01T08:15:28Z","abstract_excerpt":"In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label structure in the feature space is not clear. In this paper, we propose a novel multi-label learning method \"Structured Decomposition + Group Sparsity (SDGS)\". In SDGS, we learn a feature subspace for each label from the structured decomposition of the training data, and predict t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.0102","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":"1103.0102","created_at":"2026-05-18T04:27:27.820589+00:00"},{"alias_kind":"arxiv_version","alias_value":"1103.0102v2","created_at":"2026-05-18T04:27:27.820589+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1103.0102","created_at":"2026-05-18T04:27:27.820589+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2EGKEBRXPB7","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2EGKEBRXPB7KBQ6","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2EGKEBR","created_at":"2026-05-18T12:26:47.523578+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/Z2EGKEBRXPB7KBQ6XT26CMCF4J","json":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J.json","graph_json":"https://pith.science/api/pith-number/Z2EGKEBRXPB7KBQ6XT26CMCF4J/graph.json","events_json":"https://pith.science/api/pith-number/Z2EGKEBRXPB7KBQ6XT26CMCF4J/events.json","paper":"https://pith.science/paper/Z2EGKEBR"},"agent_actions":{"view_html":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J","download_json":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J.json","view_paper":"https://pith.science/paper/Z2EGKEBR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1103.0102&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2EGKEBRXPB7KBQ6XT26CMCF4J/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2EGKEBRXPB7KBQ6XT26CMCF4J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J/action/storage_attestation","attest_author":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J/action/author_attestation","sign_citation":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J/action/citation_signature","submit_replication":"https://pith.science/pith/Z2EGKEBRXPB7KBQ6XT26CMCF4J/action/replication_record"}},"created_at":"2026-05-18T04:27:27.820589+00:00","updated_at":"2026-05-18T04:27:27.820589+00:00"}