{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:M7QL4L4VKOSIBEGAXFYEBTZ4F4","short_pith_number":"pith:M7QL4L4V","schema_version":"1.0","canonical_sha256":"67e0be2f9553a48090c0b97040cf3c2f2402672a9f7b36153715224fd3d41df6","source":{"kind":"arxiv","id":"1011.3781","version":2},"attestation_state":"computed","paper":{"title":"Sparse PCA: Convex Relaxations, Algorithms and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alexandre d'Aspremont, Laurent El Ghaoui, Youwei Zhang","submitted_at":"2010-11-16T18:31:12Z","abstract_excerpt":"Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. Unfortunately, this problem is also combinatorially hard and we discuss convex relaxation techniques that efficiently produce good approximate solutions. We then describe several algorithms solving these relaxations as well as greedy algorithms that iterati"},"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":"1011.3781","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2010-11-16T18:31:12Z","cross_cats_sorted":[],"title_canon_sha256":"f78b4d40e16319a593c1fdb92bc94689d9dc79015028f8a21dfed2e96c29214a","abstract_canon_sha256":"767804f35be0c7fcb072cece041d9d4c0b6696b3f0bd39cd693ef442157f9c99"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:32:35.888380Z","signature_b64":"/PJAtnnvM84pEotnVd2+1Y0XpqMB46fS+DXhOAsatUmwOtiWD9mmxC2J+GOt6YF1wIaEdeRlentKvqjyEEycDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67e0be2f9553a48090c0b97040cf3c2f2402672a9f7b36153715224fd3d41df6","last_reissued_at":"2026-05-18T04:32:35.887915Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:32:35.887915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse PCA: Convex Relaxations, Algorithms and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alexandre d'Aspremont, Laurent El Ghaoui, Youwei Zhang","submitted_at":"2010-11-16T18:31:12Z","abstract_excerpt":"Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. Unfortunately, this problem is also combinatorially hard and we discuss convex relaxation techniques that efficiently produce good approximate solutions. We then describe several algorithms solving these relaxations as well as greedy algorithms that iterati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1011.3781","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":"1011.3781","created_at":"2026-05-18T04:32:35.887993+00:00"},{"alias_kind":"arxiv_version","alias_value":"1011.3781v2","created_at":"2026-05-18T04:32:35.887993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1011.3781","created_at":"2026-05-18T04:32:35.887993+00:00"},{"alias_kind":"pith_short_12","alias_value":"M7QL4L4VKOSI","created_at":"2026-05-18T12:26:10.704358+00:00"},{"alias_kind":"pith_short_16","alias_value":"M7QL4L4VKOSIBEGA","created_at":"2026-05-18T12:26:10.704358+00:00"},{"alias_kind":"pith_short_8","alias_value":"M7QL4L4V","created_at":"2026-05-18T12:26:10.704358+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/M7QL4L4VKOSIBEGAXFYEBTZ4F4","json":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4.json","graph_json":"https://pith.science/api/pith-number/M7QL4L4VKOSIBEGAXFYEBTZ4F4/graph.json","events_json":"https://pith.science/api/pith-number/M7QL4L4VKOSIBEGAXFYEBTZ4F4/events.json","paper":"https://pith.science/paper/M7QL4L4V"},"agent_actions":{"view_html":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4","download_json":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4.json","view_paper":"https://pith.science/paper/M7QL4L4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1011.3781&json=true","fetch_graph":"https://pith.science/api/pith-number/M7QL4L4VKOSIBEGAXFYEBTZ4F4/graph.json","fetch_events":"https://pith.science/api/pith-number/M7QL4L4VKOSIBEGAXFYEBTZ4F4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4/action/storage_attestation","attest_author":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4/action/author_attestation","sign_citation":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4/action/citation_signature","submit_replication":"https://pith.science/pith/M7QL4L4VKOSIBEGAXFYEBTZ4F4/action/replication_record"}},"created_at":"2026-05-18T04:32:35.887993+00:00","updated_at":"2026-05-18T04:32:35.887993+00:00"}