{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:B7GSDGB5J7NBR47L7IHO326LYB","short_pith_number":"pith:B7GSDGB5","canonical_record":{"source":{"id":"1811.00255","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-11-01T06:44:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ea642d77bca0053260590d56f1e7fab7f7c6b5e21721341cb47be9024e556d8f","abstract_canon_sha256":"fd4bf372d281331524896a95e00853337b6421fdcc2504a26e578c7664a033b8"},"schema_version":"1.0"},"canonical_sha256":"0fcd21983d4fda18f3ebfa0eedebcbc07f699b3ce13e0cfbab261f64c1ca8508","source":{"kind":"arxiv","id":"1811.00255","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.00255","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"arxiv_version","alias_value":"1811.00255v4","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00255","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"pith_short_12","alias_value":"B7GSDGB5J7NB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"B7GSDGB5J7NBR47L","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"B7GSDGB5","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:B7GSDGB5J7NBR47L7IHO326LYB","target":"record","payload":{"canonical_record":{"source":{"id":"1811.00255","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-11-01T06:44:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ea642d77bca0053260590d56f1e7fab7f7c6b5e21721341cb47be9024e556d8f","abstract_canon_sha256":"fd4bf372d281331524896a95e00853337b6421fdcc2504a26e578c7664a033b8"},"schema_version":"1.0"},"canonical_sha256":"0fcd21983d4fda18f3ebfa0eedebcbc07f699b3ce13e0cfbab261f64c1ca8508","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:58.674359Z","signature_b64":"u5E66JWtGqBs94x/XV6LC5qi1gCuw0/CkEHv3h0oqBdOf0Iix+vxvKVYxRb5p6IVi6a0SV6Fb75yLApeB7qEDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fcd21983d4fda18f3ebfa0eedebcbc07f699b3ce13e0cfbab261f64c1ca8508","last_reissued_at":"2026-05-17T23:42:58.673833Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:58.673833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.00255","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yZ+q75I7rC+p67kv4FpGDzjAkF1w4yDeC+h9fBFRQLdRUYd+QRaLo+0tN3HAp0Yvvuxx+e2G8n58fmKEJcIsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T23:27:37.796011Z"},"content_sha256":"eb7d2f13835a9eab9eb4ac81feeb79c2defab55a418d6352253b498d06b607e1","schema_version":"1.0","event_id":"sha256:eb7d2f13835a9eab9eb4ac81feeb79c2defab55a418d6352253b498d06b607e1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:B7GSDGB5J7NBR47L7IHO326LYB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HMLasso: Lasso with High Missing Rate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hironori Fujisawa, Masaaki Takada, Takeichiro Nishikawa","submitted_at":"2018-11-01T06:44:53Z","abstract_excerpt":"Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved. In this paper, we propose a novel Lasso-type regression method for high-dimensional data with high missing rates. We effectively incorporate me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00255","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"atRhBn+MBp4h4do1YTCj80T6ElyVCZEUDUEBNKHDH+yI3+ZuUG0uBc6GZA/TcmGd9BdLtYyDF1q+juxFEXpKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T23:27:37.796364Z"},"content_sha256":"396bbb119c99c272b756180807f38e8ccb11a1aabe47da3fe3fc4a053c3d2ad4","schema_version":"1.0","event_id":"sha256:396bbb119c99c272b756180807f38e8ccb11a1aabe47da3fe3fc4a053c3d2ad4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B7GSDGB5J7NBR47L7IHO326LYB/bundle.json","state_url":"https://pith.science/pith/B7GSDGB5J7NBR47L7IHO326LYB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B7GSDGB5J7NBR47L7IHO326LYB/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":"2026-06-23T23:27:37Z","links":{"resolver":"https://pith.science/pith/B7GSDGB5J7NBR47L7IHO326LYB","bundle":"https://pith.science/pith/B7GSDGB5J7NBR47L7IHO326LYB/bundle.json","state":"https://pith.science/pith/B7GSDGB5J7NBR47L7IHO326LYB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B7GSDGB5J7NBR47L7IHO326LYB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:B7GSDGB5J7NBR47L7IHO326LYB","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":"fd4bf372d281331524896a95e00853337b6421fdcc2504a26e578c7664a033b8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-11-01T06:44:53Z","title_canon_sha256":"ea642d77bca0053260590d56f1e7fab7f7c6b5e21721341cb47be9024e556d8f"},"schema_version":"1.0","source":{"id":"1811.00255","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.00255","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"arxiv_version","alias_value":"1811.00255v4","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00255","created_at":"2026-05-17T23:42:58Z"},{"alias_kind":"pith_short_12","alias_value":"B7GSDGB5J7NB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"B7GSDGB5J7NBR47L","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"B7GSDGB5","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:396bbb119c99c272b756180807f38e8ccb11a1aabe47da3fe3fc4a053c3d2ad4","target":"graph","created_at":"2026-05-17T23:42:58Z","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"},"paper":{"abstract_excerpt":"Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved. In this paper, we propose a novel Lasso-type regression method for high-dimensional data with high missing rates. We effectively incorporate me","authors_text":"Hironori Fujisawa, Masaaki Takada, Takeichiro Nishikawa","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-11-01T06:44:53Z","title":"HMLasso: Lasso with High Missing Rate"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00255","kind":"arxiv","version":4},"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:eb7d2f13835a9eab9eb4ac81feeb79c2defab55a418d6352253b498d06b607e1","target":"record","created_at":"2026-05-17T23:42:58Z","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":"fd4bf372d281331524896a95e00853337b6421fdcc2504a26e578c7664a033b8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-11-01T06:44:53Z","title_canon_sha256":"ea642d77bca0053260590d56f1e7fab7f7c6b5e21721341cb47be9024e556d8f"},"schema_version":"1.0","source":{"id":"1811.00255","kind":"arxiv","version":4}},"canonical_sha256":"0fcd21983d4fda18f3ebfa0eedebcbc07f699b3ce13e0cfbab261f64c1ca8508","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fcd21983d4fda18f3ebfa0eedebcbc07f699b3ce13e0cfbab261f64c1ca8508","first_computed_at":"2026-05-17T23:42:58.673833Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:58.673833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u5E66JWtGqBs94x/XV6LC5qi1gCuw0/CkEHv3h0oqBdOf0Iix+vxvKVYxRb5p6IVi6a0SV6Fb75yLApeB7qEDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:58.674359Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.00255","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb7d2f13835a9eab9eb4ac81feeb79c2defab55a418d6352253b498d06b607e1","sha256:396bbb119c99c272b756180807f38e8ccb11a1aabe47da3fe3fc4a053c3d2ad4"],"state_sha256":"2bfad0fe43af527433c63325f0ecbe3c8f634c349e549f973d86b2c0f22eb624"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DTtnnBREySGOhjXPxiWJ6Gz0a8k4jwCUng4Wp29kqzPRVwswHlrynupwReCnHPtC6E8w9A25aK6iyxxht0MICA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T23:27:37.798276Z","bundle_sha256":"88c6ad593abbd342c55587f2d5707b303d4cbb19cdb38e3da3fb9e9d2868be6c"}}