{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:QB2RIUYKLHLQ6UPWZZ53LZ7LA2","short_pith_number":"pith:QB2RIUYK","canonical_record":{"source":{"id":"1303.0582","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-03-03T23:41:34Z","cross_cats_sorted":[],"title_canon_sha256":"f3143262928d817dedcc24a695033df462e88710fb3fa8c05f9e017003896a1d","abstract_canon_sha256":"61f55c2ae974aa5a4de65591c18a61dbc4b26609e3a6daff226988dc1a13754b"},"schema_version":"1.0"},"canonical_sha256":"807514530a59d70f51f6ce7bb5e7eb069e1128512480a652ecc194729159380f","source":{"kind":"arxiv","id":"1303.0582","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.0582","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"arxiv_version","alias_value":"1303.0582v2","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.0582","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"pith_short_12","alias_value":"QB2RIUYKLHLQ","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"QB2RIUYKLHLQ6UPW","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"QB2RIUYK","created_at":"2026-05-18T12:27:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:QB2RIUYKLHLQ6UPWZZ53LZ7LA2","target":"record","payload":{"canonical_record":{"source":{"id":"1303.0582","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-03-03T23:41:34Z","cross_cats_sorted":[],"title_canon_sha256":"f3143262928d817dedcc24a695033df462e88710fb3fa8c05f9e017003896a1d","abstract_canon_sha256":"61f55c2ae974aa5a4de65591c18a61dbc4b26609e3a6daff226988dc1a13754b"},"schema_version":"1.0"},"canonical_sha256":"807514530a59d70f51f6ce7bb5e7eb069e1128512480a652ecc194729159380f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:51:17.612104Z","signature_b64":"3XR0CWFK/3ex2HCPGQ1vGHxioNTb0x8Iz1XGvOzo6xMDCB2PjypheaoWMdGy76AcY+r7vKfsY/CqW3o9xictAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"807514530a59d70f51f6ce7bb5e7eb069e1128512480a652ecc194729159380f","last_reissued_at":"2026-05-18T01:51:17.611502Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:51:17.611502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1303.0582","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:51:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/wL+q3j1eVEIPkLeHiwUwguf2c9R59DFPXQa/pXYP3oDWJN/gmzvbpcDX21ldPsPWkGvvNLcz8bYVB7u3NLQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:17:05.200249Z"},"content_sha256":"90422cd6ccfcb2f1100dfd5724cc7f93645acf2475423484aa54ffeebd3e8515","schema_version":"1.0","event_id":"sha256:90422cd6ccfcb2f1100dfd5724cc7f93645acf2475423484aa54ffeebd3e8515"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:QB2RIUYKLHLQ6UPWZZ53LZ7LA2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Spanias, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy","submitted_at":"2013-03-03T23:41:34Z","abstract_excerpt":"In complex visual recognition tasks it is typical to adopt multiple descriptors, that describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.0582","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:51:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bW2tr8W85gsjndt9lpwtclkZpegU6wLDYFR/YhIHzpXXycA0BOOuAKi+2SjPStzkxz4JEat52umlrNy8moRyBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:17:05.200655Z"},"content_sha256":"0f1bcb66275835b42e5dcffcff61d6702d130afef1116599777974e4b8fd3b7a","schema_version":"1.0","event_id":"sha256:0f1bcb66275835b42e5dcffcff61d6702d130afef1116599777974e4b8fd3b7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/bundle.json","state_url":"https://pith.science/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/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":"2026-05-28T21:17:05Z","links":{"resolver":"https://pith.science/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2","bundle":"https://pith.science/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/bundle.json","state":"https://pith.science/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QB2RIUYKLHLQ6UPWZZ53LZ7LA2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:QB2RIUYKLHLQ6UPWZZ53LZ7LA2","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":"61f55c2ae974aa5a4de65591c18a61dbc4b26609e3a6daff226988dc1a13754b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-03-03T23:41:34Z","title_canon_sha256":"f3143262928d817dedcc24a695033df462e88710fb3fa8c05f9e017003896a1d"},"schema_version":"1.0","source":{"id":"1303.0582","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.0582","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"arxiv_version","alias_value":"1303.0582v2","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.0582","created_at":"2026-05-18T01:51:17Z"},{"alias_kind":"pith_short_12","alias_value":"QB2RIUYKLHLQ","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"QB2RIUYKLHLQ6UPW","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"QB2RIUYK","created_at":"2026-05-18T12:27:57Z"}],"graph_snapshots":[{"event_id":"sha256:0f1bcb66275835b42e5dcffcff61d6702d130afef1116599777974e4b8fd3b7a","target":"graph","created_at":"2026-05-18T01:51:17Z","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 complex visual recognition tasks it is typical to adopt multiple descriptors, that describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel","authors_text":"Andreas Spanias, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-03-03T23:41:34Z","title":"Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.0582","kind":"arxiv","version":2},"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":"sha256:90422cd6ccfcb2f1100dfd5724cc7f93645acf2475423484aa54ffeebd3e8515","target":"record","created_at":"2026-05-18T01:51:17Z","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":"61f55c2ae974aa5a4de65591c18a61dbc4b26609e3a6daff226988dc1a13754b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-03-03T23:41:34Z","title_canon_sha256":"f3143262928d817dedcc24a695033df462e88710fb3fa8c05f9e017003896a1d"},"schema_version":"1.0","source":{"id":"1303.0582","kind":"arxiv","version":2}},"canonical_sha256":"807514530a59d70f51f6ce7bb5e7eb069e1128512480a652ecc194729159380f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"807514530a59d70f51f6ce7bb5e7eb069e1128512480a652ecc194729159380f","first_computed_at":"2026-05-18T01:51:17.611502Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:51:17.611502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3XR0CWFK/3ex2HCPGQ1vGHxioNTb0x8Iz1XGvOzo6xMDCB2PjypheaoWMdGy76AcY+r7vKfsY/CqW3o9xictAg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:51:17.612104Z","signed_message":"canonical_sha256_bytes"},"source_id":"1303.0582","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90422cd6ccfcb2f1100dfd5724cc7f93645acf2475423484aa54ffeebd3e8515","sha256:0f1bcb66275835b42e5dcffcff61d6702d130afef1116599777974e4b8fd3b7a"],"state_sha256":"b205045ab708f5cb616e23df9ae85ca1db9a2d3772434db1fb777ca91234f1f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AuIPjJ/cYAvVJakMMTdeIt5ItgFp/g6X7QOUMXNQDFSLcYrMC90Cb41tYUz/jG5+UtHZW2lLhDRmVrvZWPHIDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:17:05.203870Z","bundle_sha256":"785cdd24cac4d2f9be4603c83dc58d8dd92143dbc94602163e7b2b00082e8b63"}}