{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:2CKL4DPTNT2OEZBYMBNKTIDH4N","short_pith_number":"pith:2CKL4DPT","canonical_record":{"source":{"id":"1505.03703","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-14T12:35:19Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"fd955ef4212e5970877396729b436ed0b9483f919f9d3f3d848b9046576a9ad7","abstract_canon_sha256":"524efea527e5b3d5de73ce1ccc67eed010c702fafbdad6f8926c108532738ebe"},"schema_version":"1.0"},"canonical_sha256":"d094be0df36cf4e26438605aa9a067e347a1d266e4d71228e7fe16ea8b496fd1","source":{"kind":"arxiv","id":"1505.03703","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.03703","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"1505.03703v1","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.03703","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"2CKL4DPTNT2O","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"2CKL4DPTNT2OEZBY","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"2CKL4DPT","created_at":"2026-05-18T12:28:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:2CKL4DPTNT2OEZBYMBNKTIDH4N","target":"record","payload":{"canonical_record":{"source":{"id":"1505.03703","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-14T12:35:19Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"fd955ef4212e5970877396729b436ed0b9483f919f9d3f3d848b9046576a9ad7","abstract_canon_sha256":"524efea527e5b3d5de73ce1ccc67eed010c702fafbdad6f8926c108532738ebe"},"schema_version":"1.0"},"canonical_sha256":"d094be0df36cf4e26438605aa9a067e347a1d266e4d71228e7fe16ea8b496fd1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:09:53.261178Z","signature_b64":"gcudmfoE9PbpoAzVNI2vBX/MdMrM91s92WEg9S3ntTSw77XsD0SPJNkOSUPCFSyxJ2jztClnWvb2W9GS4erCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d094be0df36cf4e26438605aa9a067e347a1d266e4d71228e7fe16ea8b496fd1","last_reissued_at":"2026-05-18T02:09:53.260548Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:09:53.260548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1505.03703","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":"2026-05-18T02:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p9VJxoiwRAFc65QC36SSxfrbndA4xg1lrmcyamMW640N9E9UtuLlxadADXjIVb0adMZ5FDbECow5gcUxVdZ7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:17:44.293430Z"},"content_sha256":"bb9467e8f75f970f9a287a77799eb82317581fe9528022cab70c0c961f78c46f","schema_version":"1.0","event_id":"sha256:bb9467e8f75f970f9a287a77799eb82317581fe9528022cab70c0c961f78c46f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:2CKL4DPTNT2OEZBYMBNKTIDH4N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A PCA-Based Convolutional Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Guoqiang Zhong, Jun Liu, Junyu Dong, Yanhai Gan","submitted_at":"2015-05-14T12:35:19Z","abstract_excerpt":"In this paper, we propose a novel unsupervised deep learning model, called PCA-based Convolutional Network (PCN). The architecture of PCN is composed of several feature extraction stages and a nonlinear output stage. Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer. In the convolutional layer, the filter banks are simply learned by PCA. In the nonlinear output stage, binary hashing is applied. For the higher convolutional layers, the filter banks are learned from the feature maps that were obtained in the previous stage. To test"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.03703","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":"2026-05-18T02:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nBOTtrS0Z/aUuWU3hozjGp2RYeywneZdsbsY1pegAReHVzdk5hNXGfLBoF2wgYltoASSa7IcKF1YrL1VOYnZCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:17:44.293781Z"},"content_sha256":"08b31747491195439ed6206e86920d4dfecf89c816a4966b67816a1d8a0bd0a8","schema_version":"1.0","event_id":"sha256:08b31747491195439ed6206e86920d4dfecf89c816a4966b67816a1d8a0bd0a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/bundle.json","state_url":"https://pith.science/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/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":"2026-06-03T21:17:44Z","links":{"resolver":"https://pith.science/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N","bundle":"https://pith.science/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/bundle.json","state":"https://pith.science/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2CKL4DPTNT2OEZBYMBNKTIDH4N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:2CKL4DPTNT2OEZBYMBNKTIDH4N","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":"524efea527e5b3d5de73ce1ccc67eed010c702fafbdad6f8926c108532738ebe","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-14T12:35:19Z","title_canon_sha256":"fd955ef4212e5970877396729b436ed0b9483f919f9d3f3d848b9046576a9ad7"},"schema_version":"1.0","source":{"id":"1505.03703","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.03703","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"1505.03703v1","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.03703","created_at":"2026-05-18T02:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"2CKL4DPTNT2O","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"2CKL4DPTNT2OEZBY","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"2CKL4DPT","created_at":"2026-05-18T12:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:08b31747491195439ed6206e86920d4dfecf89c816a4966b67816a1d8a0bd0a8","target":"graph","created_at":"2026-05-18T02:09:53Z","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 propose a novel unsupervised deep learning model, called PCA-based Convolutional Network (PCN). The architecture of PCN is composed of several feature extraction stages and a nonlinear output stage. Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer. In the convolutional layer, the filter banks are simply learned by PCA. In the nonlinear output stage, binary hashing is applied. For the higher convolutional layers, the filter banks are learned from the feature maps that were obtained in the previous stage. To test","authors_text":"Guoqiang Zhong, Jun Liu, Junyu Dong, Yanhai Gan","cross_cats":["cs.CV","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-14T12:35:19Z","title":"A PCA-Based Convolutional Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.03703","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:bb9467e8f75f970f9a287a77799eb82317581fe9528022cab70c0c961f78c46f","target":"record","created_at":"2026-05-18T02:09:53Z","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":"524efea527e5b3d5de73ce1ccc67eed010c702fafbdad6f8926c108532738ebe","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-14T12:35:19Z","title_canon_sha256":"fd955ef4212e5970877396729b436ed0b9483f919f9d3f3d848b9046576a9ad7"},"schema_version":"1.0","source":{"id":"1505.03703","kind":"arxiv","version":1}},"canonical_sha256":"d094be0df36cf4e26438605aa9a067e347a1d266e4d71228e7fe16ea8b496fd1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d094be0df36cf4e26438605aa9a067e347a1d266e4d71228e7fe16ea8b496fd1","first_computed_at":"2026-05-18T02:09:53.260548Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:09:53.260548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gcudmfoE9PbpoAzVNI2vBX/MdMrM91s92WEg9S3ntTSw77XsD0SPJNkOSUPCFSyxJ2jztClnWvb2W9GS4erCDA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:09:53.261178Z","signed_message":"canonical_sha256_bytes"},"source_id":"1505.03703","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb9467e8f75f970f9a287a77799eb82317581fe9528022cab70c0c961f78c46f","sha256:08b31747491195439ed6206e86920d4dfecf89c816a4966b67816a1d8a0bd0a8"],"state_sha256":"1720fb6610b1e2d1773778b3839ff599ace512064924e8cbb9b7415b919b4875"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VhVpZ3tkPCHBnE9ucVxfzJfk6p6cQ+XK98fLRpMSAK17ha4htJWuRK4lqhcu1EaCBo+GnG5TnYQfP5Xb3dKlAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T21:17:44.295772Z","bundle_sha256":"f5d99cb83e1739dfbe062fec611696f1fd05fb50f22feae0795db727ec8f7864"}}