{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CIFWO2NS4ONFOXKHOENHYMQLXV","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":"b0377c3aee4babdacec1f2cdc956a11570fb80f482ef2c83f24a49cc5d787a3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-06-26T02:51:36Z","title_canon_sha256":"27ea8c002ba39d760a8aa7f6575b42699a371c5f307c1109ee0e4ea536a40433"},"schema_version":"1.0","source":{"id":"2306.14388","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.14388","created_at":"2026-07-05T06:24:35Z"},{"alias_kind":"arxiv_version","alias_value":"2306.14388v1","created_at":"2026-07-05T06:24:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.14388","created_at":"2026-07-05T06:24:35Z"},{"alias_kind":"pith_short_12","alias_value":"CIFWO2NS4ONF","created_at":"2026-07-05T06:24:35Z"},{"alias_kind":"pith_short_16","alias_value":"CIFWO2NS4ONFOXKH","created_at":"2026-07-05T06:24:35Z"},{"alias_kind":"pith_short_8","alias_value":"CIFWO2NS","created_at":"2026-07-05T06:24:35Z"}],"graph_snapshots":[{"event_id":"sha256:7de3dfbcfad5fbe77b97c5afa194e267e8ad1bfcd15d33c7ca73bce0a6177bc9","target":"graph","created_at":"2026-07-05T06:24:35Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.14388/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Functional principal component analysis (FPCA) is an important technique for dimension reduction in functional data analysis (FDA). Classical FPCA method is based on the Karhunen-Lo\\`{e}ve expansion, which assumes a linear structure of the observed functional data. However, the assumption may not always be satisfied, and the FPCA method can become inefficient when the data deviates from the linear assumption. In this paper, we propose a novel FPCA method that is suitable for data with a nonlinear structure by neural network approach. We construct networks that can be applied to functional data","authors_text":"Chunming Zhang, Jingxiao Zhang, Rou Zhong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-06-26T02:51:36Z","title":"Nonlinear Functional Principal Component Analysis Using Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.14388","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:fe560ea94d0087b0f29da8efb31d7fdcff8d5a394c725b753de9adf728c19cd0","target":"record","created_at":"2026-07-05T06:24:35Z","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":"b0377c3aee4babdacec1f2cdc956a11570fb80f482ef2c83f24a49cc5d787a3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-06-26T02:51:36Z","title_canon_sha256":"27ea8c002ba39d760a8aa7f6575b42699a371c5f307c1109ee0e4ea536a40433"},"schema_version":"1.0","source":{"id":"2306.14388","kind":"arxiv","version":1}},"canonical_sha256":"120b6769b2e39a575d47711a7c320bbd61c77dbfb800bdc8148efe73d7e8d849","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"120b6769b2e39a575d47711a7c320bbd61c77dbfb800bdc8148efe73d7e8d849","first_computed_at":"2026-07-05T06:24:35.852092Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:24:35.852092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n9OVhEiBT2RJnWCbQeiaSpnHT/sLHIj7S8YZ+HlUkeKHP4XF8LKYaOSBBG+dIQnYy0j4dyoC3SOc8iMPTMLACA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:24:35.852469Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.14388","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe560ea94d0087b0f29da8efb31d7fdcff8d5a394c725b753de9adf728c19cd0","sha256:7de3dfbcfad5fbe77b97c5afa194e267e8ad1bfcd15d33c7ca73bce0a6177bc9"],"state_sha256":"e66232c5c2fb6a0640aa281995b4f7907778f83005aa1f0fa48b7587959f78ce"}