{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XLWCNZWPI3UCOAK4QDUJ62DJ4K","short_pith_number":"pith:XLWCNZWP","schema_version":"1.0","canonical_sha256":"baec26e6cf46e827015c80e89f6869e2806ae9530c897270d4031acd833c4c9a","source":{"kind":"arxiv","id":"1903.03891","version":2},"attestation_state":"computed","paper":{"title":"Non-Negative Kernel Sparse Coding for the Classification of Motion Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Babak Hosseini, Barbara Hammer, Felix H\\\"ulsmann, Mario Botsch","submitted_at":"2019-03-10T00:45:05Z","abstract_excerpt":"We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear rep"},"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":"1903.03891","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-10T00:45:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"caa6d8c56a050af603ffb3c35942bcac5ae20aeddcdc2f08403d8ac0a781af43","abstract_canon_sha256":"846dd52b33f7a6b787bfab8a418fb6a75878002834ff620eab224c7701a813da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:29.518712Z","signature_b64":"anCLaLUBEWxfhrlFxbN7rpDfrbxFyiwaZtu3m9xWHmYARSVzxT68LniA9vFtw5mmaSkIxSBMonTpYzRRHRgJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"baec26e6cf46e827015c80e89f6869e2806ae9530c897270d4031acd833c4c9a","last_reissued_at":"2026-05-17T23:51:29.518276Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:29.518276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-Negative Kernel Sparse Coding for the Classification of Motion Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Babak Hosseini, Barbara Hammer, Felix H\\\"ulsmann, Mario Botsch","submitted_at":"2019-03-10T00:45:05Z","abstract_excerpt":"We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear rep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.03891","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":"1903.03891","created_at":"2026-05-17T23:51:29.518339+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.03891v2","created_at":"2026-05-17T23:51:29.518339+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.03891","created_at":"2026-05-17T23:51:29.518339+00:00"},{"alias_kind":"pith_short_12","alias_value":"XLWCNZWPI3UC","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XLWCNZWPI3UCOAK4","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XLWCNZWP","created_at":"2026-05-18T12:33:33.725879+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/XLWCNZWPI3UCOAK4QDUJ62DJ4K","json":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K.json","graph_json":"https://pith.science/api/pith-number/XLWCNZWPI3UCOAK4QDUJ62DJ4K/graph.json","events_json":"https://pith.science/api/pith-number/XLWCNZWPI3UCOAK4QDUJ62DJ4K/events.json","paper":"https://pith.science/paper/XLWCNZWP"},"agent_actions":{"view_html":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K","download_json":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K.json","view_paper":"https://pith.science/paper/XLWCNZWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.03891&json=true","fetch_graph":"https://pith.science/api/pith-number/XLWCNZWPI3UCOAK4QDUJ62DJ4K/graph.json","fetch_events":"https://pith.science/api/pith-number/XLWCNZWPI3UCOAK4QDUJ62DJ4K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K/action/storage_attestation","attest_author":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K/action/author_attestation","sign_citation":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K/action/citation_signature","submit_replication":"https://pith.science/pith/XLWCNZWPI3UCOAK4QDUJ62DJ4K/action/replication_record"}},"created_at":"2026-05-17T23:51:29.518339+00:00","updated_at":"2026-05-17T23:51:29.518339+00:00"}