{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QVNL52S5YTQ2TCI232UT3LW7L5","short_pith_number":"pith:QVNL52S5","canonical_record":{"source":{"id":"1710.10695","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-29T21:17:09Z","cross_cats_sorted":[],"title_canon_sha256":"6f46877511d03a1c4b93d739f975a5d2d0a3cdb63f5f35ab0fe7de40ab8dd4b9","abstract_canon_sha256":"97269cd7dfb27c4efd0c2f0bbd0dbe72ff9f9f2870b86741ad8f8847bdf81d29"},"schema_version":"1.0"},"canonical_sha256":"855abeea5dc4e1a9891adea93daedf5f7f05d0029a68a5311efe7d4fc379ad7b","source":{"kind":"arxiv","id":"1710.10695","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10695","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10695v1","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10695","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"QVNL52S5YTQ2","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QVNL52S5YTQ2TCI2","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QVNL52S5","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QVNL52S5YTQ2TCI232UT3LW7L5","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10695","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-29T21:17:09Z","cross_cats_sorted":[],"title_canon_sha256":"6f46877511d03a1c4b93d739f975a5d2d0a3cdb63f5f35ab0fe7de40ab8dd4b9","abstract_canon_sha256":"97269cd7dfb27c4efd0c2f0bbd0dbe72ff9f9f2870b86741ad8f8847bdf81d29"},"schema_version":"1.0"},"canonical_sha256":"855abeea5dc4e1a9891adea93daedf5f7f05d0029a68a5311efe7d4fc379ad7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:28.188568Z","signature_b64":"nCpOOaQgycHhVCjtyddYfcC0xCMuYhd5Y0Ub+AGWH7oDLU3j92aeekJMBSTk8hOvQgJ7sKCXv3Ks5CURFKhhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"855abeea5dc4e1a9891adea93daedf5f7f05d0029a68a5311efe7d4fc379ad7b","last_reissued_at":"2026-05-18T00:11:28.188138Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:28.188138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10695","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":"2026-05-18T00:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rn0u7SUTk2ELK234A5m3FNrXeP0Soy/kEGO6e/yZssCp44I54sQdULvGWo9i45JBuz645a7LYRlN5U1uHAeHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T20:28:09.517268Z"},"content_sha256":"5e4ca88150ea091ed372de67bc415500b2377b7a1ad52dcd8047c7e833cf1ded","schema_version":"1.0","event_id":"sha256:5e4ca88150ea091ed372de67bc415500b2377b7a1ad52dcd8047c7e833cf1ded"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QVNL52S5YTQ2TCI232UT3LW7L5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multilinear Class-Specific Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandros Iosifidis, Dat Thanh Tran, Moncef Gabbouj","submitted_at":"2017-10-29T21:17:09Z","abstract_excerpt":"There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10695","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":"2026-05-18T00:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"taoYMJdU/RpEGikoMxOoG7QaxTTR9iOKJzspePly9Alqh8u0Yq5iNHL7hSr6AWtK/762GwdhHAWL64O2QPm/DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T20:28:09.517639Z"},"content_sha256":"47e809eedae0ad4c520d6f598a1e5c8a012f4abbe8c0e86798b2b0496282bf63","schema_version":"1.0","event_id":"sha256:47e809eedae0ad4c520d6f598a1e5c8a012f4abbe8c0e86798b2b0496282bf63"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QVNL52S5YTQ2TCI232UT3LW7L5/bundle.json","state_url":"https://pith.science/pith/QVNL52S5YTQ2TCI232UT3LW7L5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QVNL52S5YTQ2TCI232UT3LW7L5/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":"2026-06-04T20:28:09Z","links":{"resolver":"https://pith.science/pith/QVNL52S5YTQ2TCI232UT3LW7L5","bundle":"https://pith.science/pith/QVNL52S5YTQ2TCI232UT3LW7L5/bundle.json","state":"https://pith.science/pith/QVNL52S5YTQ2TCI232UT3LW7L5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QVNL52S5YTQ2TCI232UT3LW7L5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QVNL52S5YTQ2TCI232UT3LW7L5","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":"97269cd7dfb27c4efd0c2f0bbd0dbe72ff9f9f2870b86741ad8f8847bdf81d29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-29T21:17:09Z","title_canon_sha256":"6f46877511d03a1c4b93d739f975a5d2d0a3cdb63f5f35ab0fe7de40ab8dd4b9"},"schema_version":"1.0","source":{"id":"1710.10695","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10695","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10695v1","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10695","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"QVNL52S5YTQ2","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QVNL52S5YTQ2TCI2","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QVNL52S5","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:47e809eedae0ad4c520d6f598a1e5c8a012f4abbe8c0e86798b2b0496282bf63","target":"graph","created_at":"2026-05-18T00:11:28Z","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":"There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the di","authors_text":"Alexandros Iosifidis, Dat Thanh Tran, Moncef Gabbouj","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-29T21:17:09Z","title":"Multilinear Class-Specific Discriminant Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10695","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":"sha256:5e4ca88150ea091ed372de67bc415500b2377b7a1ad52dcd8047c7e833cf1ded","target":"record","created_at":"2026-05-18T00:11:28Z","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":"97269cd7dfb27c4efd0c2f0bbd0dbe72ff9f9f2870b86741ad8f8847bdf81d29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-29T21:17:09Z","title_canon_sha256":"6f46877511d03a1c4b93d739f975a5d2d0a3cdb63f5f35ab0fe7de40ab8dd4b9"},"schema_version":"1.0","source":{"id":"1710.10695","kind":"arxiv","version":1}},"canonical_sha256":"855abeea5dc4e1a9891adea93daedf5f7f05d0029a68a5311efe7d4fc379ad7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"855abeea5dc4e1a9891adea93daedf5f7f05d0029a68a5311efe7d4fc379ad7b","first_computed_at":"2026-05-18T00:11:28.188138Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:28.188138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nCpOOaQgycHhVCjtyddYfcC0xCMuYhd5Y0Ub+AGWH7oDLU3j92aeekJMBSTk8hOvQgJ7sKCXv3Ks5CURFKhhCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:28.188568Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10695","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e4ca88150ea091ed372de67bc415500b2377b7a1ad52dcd8047c7e833cf1ded","sha256:47e809eedae0ad4c520d6f598a1e5c8a012f4abbe8c0e86798b2b0496282bf63"],"state_sha256":"7687aa2e6f01915fff8816995bd62348df8f627fe5cce5bd89c8f37e93d6fbf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t9Ga9N21tFBgFZluvvdWD+KHsNJy1BE7pqF8AsI0J6uJuf7EPDTxYxYOPOneGbLI+hrtNvwB6+qvG2JsIjzUCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T20:28:09.519935Z","bundle_sha256":"87d38d72d96a573bd5d328e761ed8384beaa28c87ae868412273e26c022d0d0a"}}