{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:NSJDXWFOYC5GYPHHVHFAOPJDB2","short_pith_number":"pith:NSJDXWFO","schema_version":"1.0","canonical_sha256":"6c923bd8aec0ba6c3ce7a9ca073d230e98ff360859302189e336b4d93f11b079","source":{"kind":"arxiv","id":"1502.04168","version":2},"attestation_state":"computed","paper":{"title":"Nonparametric regression using needlet kernels for spherical data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Shaobo Lin","submitted_at":"2015-02-14T05:37:32Z","abstract_excerpt":"Needlets have been recognized as state-of-the-art tools to tackle spherical data, due to their excellent localization properties in both spacial and frequency domains.\n  This paper considers developing kernel methods associated with the needlet kernel for nonparametric regression problems whose predictor variables are defined on a sphere. Due to the localization property in the frequency domain, we prove that the regularization parameter of the kernel ridge regression associated with the needlet kernel can decrease arbitrarily fast. A natural consequence is that the regularization term for the"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1502.04168","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T05:37:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"43e289014f69deee5ed503d0780777bea1c55554f52f05c55eced43f66a61709","abstract_canon_sha256":"3a342a7f9dbb4c68ad585280558a9b60cef5f4fa3f99e34b823b60ab02f327de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:33:32.328510Z","signature_b64":"6AO/NPyYSqCRwppyNeeG3cbBDlwYCtMs2PjNg4YqWWR0uPP478XX2J+VI70/E9EV0DuvhdTFY+YgqxElVtNrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c923bd8aec0ba6c3ce7a9ca073d230e98ff360859302189e336b4d93f11b079","last_reissued_at":"2026-05-18T01:33:32.327831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:33:32.327831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric regression using needlet kernels for spherical data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Shaobo Lin","submitted_at":"2015-02-14T05:37:32Z","abstract_excerpt":"Needlets have been recognized as state-of-the-art tools to tackle spherical data, due to their excellent localization properties in both spacial and frequency domains.\n  This paper considers developing kernel methods associated with the needlet kernel for nonparametric regression problems whose predictor variables are defined on a sphere. Due to the localization property in the frequency domain, we prove that the regularization parameter of the kernel ridge regression associated with the needlet kernel can decrease arbitrarily fast. A natural consequence is that the regularization term for the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04168","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1502.04168","created_at":"2026-05-18T01:33:32.327937+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.04168v2","created_at":"2026-05-18T01:33:32.327937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04168","created_at":"2026-05-18T01:33:32.327937+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSJDXWFOYC5G","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSJDXWFOYC5GYPHH","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSJDXWFO","created_at":"2026-05-18T12:29:34.919912+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2","json":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2.json","graph_json":"https://pith.science/api/pith-number/NSJDXWFOYC5GYPHHVHFAOPJDB2/graph.json","events_json":"https://pith.science/api/pith-number/NSJDXWFOYC5GYPHHVHFAOPJDB2/events.json","paper":"https://pith.science/paper/NSJDXWFO"},"agent_actions":{"view_html":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2","download_json":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2.json","view_paper":"https://pith.science/paper/NSJDXWFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.04168&json=true","fetch_graph":"https://pith.science/api/pith-number/NSJDXWFOYC5GYPHHVHFAOPJDB2/graph.json","fetch_events":"https://pith.science/api/pith-number/NSJDXWFOYC5GYPHHVHFAOPJDB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2/action/storage_attestation","attest_author":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2/action/author_attestation","sign_citation":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2/action/citation_signature","submit_replication":"https://pith.science/pith/NSJDXWFOYC5GYPHHVHFAOPJDB2/action/replication_record"}},"created_at":"2026-05-18T01:33:32.327937+00:00","updated_at":"2026-05-18T01:33:32.327937+00:00"}