{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:BM3UN4S6IFXXOSHRYVY7IC5NIZ","short_pith_number":"pith:BM3UN4S6","canonical_record":{"source":{"id":"1606.07289","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-23T12:36:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"955028d7d921f32f2b25f6baeb9e5435a5c228bc087b7d8ef30a95db55c1f9db","abstract_canon_sha256":"ce1914a1dd0355523abf2ebe10c3da6141a090d0de8a6f78614eb44fd05f62a5"},"schema_version":"1.0"},"canonical_sha256":"0b3746f25e416f7748f1c571f40bad46667c16146f5381faefc755ed42f73de3","source":{"kind":"arxiv","id":"1606.07289","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07289","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07289v1","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07289","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"pith_short_12","alias_value":"BM3UN4S6IFXX","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BM3UN4S6IFXXOSHR","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BM3UN4S6","created_at":"2026-05-18T12:30:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:BM3UN4S6IFXXOSHRYVY7IC5NIZ","target":"record","payload":{"canonical_record":{"source":{"id":"1606.07289","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-23T12:36:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"955028d7d921f32f2b25f6baeb9e5435a5c228bc087b7d8ef30a95db55c1f9db","abstract_canon_sha256":"ce1914a1dd0355523abf2ebe10c3da6141a090d0de8a6f78614eb44fd05f62a5"},"schema_version":"1.0"},"canonical_sha256":"0b3746f25e416f7748f1c571f40bad46667c16146f5381faefc755ed42f73de3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:34.569561Z","signature_b64":"KXTn80gT3HVZi6tb5wB0R39SS6UBGmEsB2itZUSo++FcgqWGox2sol8Bxx3/lOUmNzJSCMKx7w3TDwJ0zXusCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b3746f25e416f7748f1c571f40bad46667c16146f5381faefc755ed42f73de3","last_reissued_at":"2026-05-18T01:00:34.568991Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:34.568991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.07289","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":"2026-05-18T01:00:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bN1oUFLhnSCI5yju3KtefjOF4hfAcophrJ3DPkcJWR2nmpgeb9AwmnSCMhphvgUCA6pEpJeafNWehU6DFZpGDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:27:49.600414Z"},"content_sha256":"7fabf5801cf63f24d89f6a6dce0a83174e80c110040171f1d16689fe6cbb1684","schema_version":"1.0","event_id":"sha256:7fabf5801cf63f24d89f6a6dce0a83174e80c110040171f1d16689fe6cbb1684"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:BM3UN4S6IFXXOSHRYVY7IC5NIZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Non-convex regularization in remote sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Devis Tuia, Michel Barlaud, Remi Flamary","submitted_at":"2016-06-23T12:36:01Z","abstract_excerpt":"In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). We compare their properties and advantages on several classification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07289","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":""},"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":"2026-05-18T01:00:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mtXH8gzlURJmmAUmRcgtrJPiLxDEdcU7MqDhTV81ChIw0eOj5gXN9us7MI1R/fWpeEuQeSHGTIqNGiDzCypiAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:27:49.601082Z"},"content_sha256":"8c8a5453e9fda719e85ce275a428368eaed3c9f783d95d691225450afbf30961","schema_version":"1.0","event_id":"sha256:8c8a5453e9fda719e85ce275a428368eaed3c9f783d95d691225450afbf30961"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/bundle.json","state_url":"https://pith.science/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/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":"2026-05-25T21:27:49Z","links":{"resolver":"https://pith.science/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ","bundle":"https://pith.science/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/bundle.json","state":"https://pith.science/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BM3UN4S6IFXXOSHRYVY7IC5NIZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:BM3UN4S6IFXXOSHRYVY7IC5NIZ","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":"ce1914a1dd0355523abf2ebe10c3da6141a090d0de8a6f78614eb44fd05f62a5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-23T12:36:01Z","title_canon_sha256":"955028d7d921f32f2b25f6baeb9e5435a5c228bc087b7d8ef30a95db55c1f9db"},"schema_version":"1.0","source":{"id":"1606.07289","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07289","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07289v1","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07289","created_at":"2026-05-18T01:00:34Z"},{"alias_kind":"pith_short_12","alias_value":"BM3UN4S6IFXX","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BM3UN4S6IFXXOSHR","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BM3UN4S6","created_at":"2026-05-18T12:30:07Z"}],"graph_snapshots":[{"event_id":"sha256:8c8a5453e9fda719e85ce275a428368eaed3c9f783d95d691225450afbf30961","target":"graph","created_at":"2026-05-18T01:00:34Z","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":"In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). We compare their properties and advantages on several classification","authors_text":"Devis Tuia, Michel Barlaud, Remi Flamary","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-23T12:36:01Z","title":"Non-convex regularization in remote sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07289","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":"sha256:7fabf5801cf63f24d89f6a6dce0a83174e80c110040171f1d16689fe6cbb1684","target":"record","created_at":"2026-05-18T01:00:34Z","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":"ce1914a1dd0355523abf2ebe10c3da6141a090d0de8a6f78614eb44fd05f62a5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-23T12:36:01Z","title_canon_sha256":"955028d7d921f32f2b25f6baeb9e5435a5c228bc087b7d8ef30a95db55c1f9db"},"schema_version":"1.0","source":{"id":"1606.07289","kind":"arxiv","version":1}},"canonical_sha256":"0b3746f25e416f7748f1c571f40bad46667c16146f5381faefc755ed42f73de3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b3746f25e416f7748f1c571f40bad46667c16146f5381faefc755ed42f73de3","first_computed_at":"2026-05-18T01:00:34.568991Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:34.568991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KXTn80gT3HVZi6tb5wB0R39SS6UBGmEsB2itZUSo++FcgqWGox2sol8Bxx3/lOUmNzJSCMKx7w3TDwJ0zXusCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:34.569561Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.07289","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7fabf5801cf63f24d89f6a6dce0a83174e80c110040171f1d16689fe6cbb1684","sha256:8c8a5453e9fda719e85ce275a428368eaed3c9f783d95d691225450afbf30961"],"state_sha256":"ea61b40dcb26e75d29096540ea94d1eeb20180e1faab644a66ea49e71cc08d5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qp3Xw0ZkcYr+cy+cXaTprW2Ik5oVzWCdWx2d7O4NbfQqhUKtFw0bwRyU4v3/NVWbGNs8+rfCmR17M6LruSynAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:27:49.604615Z","bundle_sha256":"d7769d0922bd05b64231cb9b0ab659a66fcf7e39746171866692d0e8ca267433"}}