{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5EWM3NJC7MTTVYCC6RYEEQAEP4","short_pith_number":"pith:5EWM3NJC","schema_version":"1.0","canonical_sha256":"e92ccdb522fb273ae042f4704240047f2661d0005df5b35bb4c8e2ec6282bbb0","source":{"kind":"arxiv","id":"1709.08164","version":2},"attestation_state":"computed","paper":{"title":"Tensor-Based Classifiers for Hyperspectral Data Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anastasios Doulamis, Antonis Nikitakis, Konstantinos Makantasis, Nikolaos Doulamis","submitted_at":"2017-09-24T09:05:36Z","abstract_excerpt":"In this work, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfies the {\\it rank}-1 canonical decomposition property. Then, we introduce learning algorithms to train both the linear and the non-linear classifier in a way to i) to minimize the error over the training samples and ii) the weight coefficients satisfies the {\\it rank}-1 canonical decomposition property. The advantages of the proposed classification mod"},"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":"1709.08164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-24T09:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"2ce4e0188333e701c34c96c9e29eeede99fff3d6931fc940d8a1cb69ff1e0b51","abstract_canon_sha256":"f7657f4aacb50ae101242aef39072c7b74a0380c3d431b33e7034e48b7110406"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:44.416466Z","signature_b64":"SAtu0DvGUngyU2xPfViuFjqx3v5jySTV/dEM+Fvl0ZQOeGGQcIAbit/jiwhjzM1OILrQHLFpSPJLWmFB2bmuBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e92ccdb522fb273ae042f4704240047f2661d0005df5b35bb4c8e2ec6282bbb0","last_reissued_at":"2026-05-17T23:57:44.415937Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:44.415937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor-Based Classifiers for Hyperspectral Data Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anastasios Doulamis, Antonis Nikitakis, Konstantinos Makantasis, Nikolaos Doulamis","submitted_at":"2017-09-24T09:05:36Z","abstract_excerpt":"In this work, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfies the {\\it rank}-1 canonical decomposition property. Then, we introduce learning algorithms to train both the linear and the non-linear classifier in a way to i) to minimize the error over the training samples and ii) the weight coefficients satisfies the {\\it rank}-1 canonical decomposition property. The advantages of the proposed classification mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08164","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":"1709.08164","created_at":"2026-05-17T23:57:44.416026+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08164v2","created_at":"2026-05-17T23:57:44.416026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08164","created_at":"2026-05-17T23:57:44.416026+00:00"},{"alias_kind":"pith_short_12","alias_value":"5EWM3NJC7MTT","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5EWM3NJC7MTTVYCC","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5EWM3NJC","created_at":"2026-05-18T12:31:00.734936+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/5EWM3NJC7MTTVYCC6RYEEQAEP4","json":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4.json","graph_json":"https://pith.science/api/pith-number/5EWM3NJC7MTTVYCC6RYEEQAEP4/graph.json","events_json":"https://pith.science/api/pith-number/5EWM3NJC7MTTVYCC6RYEEQAEP4/events.json","paper":"https://pith.science/paper/5EWM3NJC"},"agent_actions":{"view_html":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4","download_json":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4.json","view_paper":"https://pith.science/paper/5EWM3NJC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08164&json=true","fetch_graph":"https://pith.science/api/pith-number/5EWM3NJC7MTTVYCC6RYEEQAEP4/graph.json","fetch_events":"https://pith.science/api/pith-number/5EWM3NJC7MTTVYCC6RYEEQAEP4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4/action/storage_attestation","attest_author":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4/action/author_attestation","sign_citation":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4/action/citation_signature","submit_replication":"https://pith.science/pith/5EWM3NJC7MTTVYCC6RYEEQAEP4/action/replication_record"}},"created_at":"2026-05-17T23:57:44.416026+00:00","updated_at":"2026-05-17T23:57:44.416026+00:00"}