{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6A5OZ7CCTEZ3A2HENHHWE7FM5Z","short_pith_number":"pith:6A5OZ7CC","canonical_record":{"source":{"id":"1910.08790","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-19T15:45:15Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"a70682723e7c4237f0518388096fce63cba2b4fb1a362267e816363d9e0de8bf","abstract_canon_sha256":"3400e58cdcd66d446ead39907526816a20f35df0a47559e9364a764ad4f3b190"},"schema_version":"1.0"},"canonical_sha256":"f03aecfc429933b068e469cf627cacee4932d964f4fc8534adaaf78ac0270639","source":{"kind":"arxiv","id":"1910.08790","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.08790","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"arxiv_version","alias_value":"1910.08790v2","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.08790","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_12","alias_value":"6A5OZ7CCTEZ3","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_16","alias_value":"6A5OZ7CCTEZ3A2HE","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_8","alias_value":"6A5OZ7CC","created_at":"2026-07-05T01:01:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6A5OZ7CCTEZ3A2HENHHWE7FM5Z","target":"record","payload":{"canonical_record":{"source":{"id":"1910.08790","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-19T15:45:15Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"a70682723e7c4237f0518388096fce63cba2b4fb1a362267e816363d9e0de8bf","abstract_canon_sha256":"3400e58cdcd66d446ead39907526816a20f35df0a47559e9364a764ad4f3b190"},"schema_version":"1.0"},"canonical_sha256":"f03aecfc429933b068e469cf627cacee4932d964f4fc8534adaaf78ac0270639","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:01:36.343781Z","signature_b64":"w+h5jb9F/fSuguzWHNudJFhVgwI1OYfaFhziD51e5Xea+++ox3pRQtmSIEQmYWhf3P7qfOqCojAXwCTKIJwpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f03aecfc429933b068e469cf627cacee4932d964f4fc8534adaaf78ac0270639","last_reissued_at":"2026-07-05T01:01:36.343159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:01:36.343159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.08790","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:01:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iaBgifdkFeDzWP7GvF3OOX90Tb0iITOWHCsOm3wqaCaMuPb8+mXKW4IRNGqsIktyOBIB4rBz8PO5HmDfqOCrAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:51:11.049187Z"},"content_sha256":"1ecd0dcc6744195e3f94afece6e8b71826430fe9ecb0a7a62f423aac35862014","schema_version":"1.0","event_id":"sha256:1ecd0dcc6744195e3f94afece6e8b71826430fe9ecb0a7a62f423aac35862014"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6A5OZ7CCTEZ3A2HENHHWE7FM5Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Biplab Banerjee, Krishna Mohan Buddhiraju, Megh Shukla","submitted_at":"2019-10-19T15:45:15Z","abstract_excerpt":"Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.08790","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1910.08790/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:01:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RqX9RaV0rR9PkQLpLyGM9u4xUAaSzTwQywWjpQhzKsLCIlkxcznTPhRU3S6hsb4BVKOEgWmsil7CqxP5UD8tDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:51:11.049572Z"},"content_sha256":"cdf9404d3b74a2b3af913115064e3eb2a2441e9c7be8ab13e4e9d1f1838169ff","schema_version":"1.0","event_id":"sha256:cdf9404d3b74a2b3af913115064e3eb2a2441e9c7be8ab13e4e9d1f1838169ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/bundle.json","state_url":"https://pith.science/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/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":"2026-07-07T13:51:11Z","links":{"resolver":"https://pith.science/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z","bundle":"https://pith.science/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/bundle.json","state":"https://pith.science/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6A5OZ7CCTEZ3A2HENHHWE7FM5Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6A5OZ7CCTEZ3A2HENHHWE7FM5Z","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":"3400e58cdcd66d446ead39907526816a20f35df0a47559e9364a764ad4f3b190","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-19T15:45:15Z","title_canon_sha256":"a70682723e7c4237f0518388096fce63cba2b4fb1a362267e816363d9e0de8bf"},"schema_version":"1.0","source":{"id":"1910.08790","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.08790","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"arxiv_version","alias_value":"1910.08790v2","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.08790","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_12","alias_value":"6A5OZ7CCTEZ3","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_16","alias_value":"6A5OZ7CCTEZ3A2HE","created_at":"2026-07-05T01:01:36Z"},{"alias_kind":"pith_short_8","alias_value":"6A5OZ7CC","created_at":"2026-07-05T01:01:36Z"}],"graph_snapshots":[{"event_id":"sha256:cdf9404d3b74a2b3af913115064e3eb2a2441e9c7be8ab13e4e9d1f1838169ff","target":"graph","created_at":"2026-07-05T01:01:36Z","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/1910.08790/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps","authors_text":"Biplab Banerjee, Krishna Mohan Buddhiraju, Megh Shukla","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-19T15:45:15Z","title":"LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral Imagery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.08790","kind":"arxiv","version":2},"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":"sha256:1ecd0dcc6744195e3f94afece6e8b71826430fe9ecb0a7a62f423aac35862014","target":"record","created_at":"2026-07-05T01:01:36Z","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":"3400e58cdcd66d446ead39907526816a20f35df0a47559e9364a764ad4f3b190","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-19T15:45:15Z","title_canon_sha256":"a70682723e7c4237f0518388096fce63cba2b4fb1a362267e816363d9e0de8bf"},"schema_version":"1.0","source":{"id":"1910.08790","kind":"arxiv","version":2}},"canonical_sha256":"f03aecfc429933b068e469cf627cacee4932d964f4fc8534adaaf78ac0270639","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f03aecfc429933b068e469cf627cacee4932d964f4fc8534adaaf78ac0270639","first_computed_at":"2026-07-05T01:01:36.343159Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:01:36.343159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w+h5jb9F/fSuguzWHNudJFhVgwI1OYfaFhziD51e5Xea+++ox3pRQtmSIEQmYWhf3P7qfOqCojAXwCTKIJwpDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:01:36.343781Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.08790","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ecd0dcc6744195e3f94afece6e8b71826430fe9ecb0a7a62f423aac35862014","sha256:cdf9404d3b74a2b3af913115064e3eb2a2441e9c7be8ab13e4e9d1f1838169ff"],"state_sha256":"2dbfa94ede7c51e1bded16f12f913b6e8bec2534fe84734cb3149f007a515d9c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OupnRYRpGt9bGXNYicpYaGHJopbwIClcfOd+leiXZAjMKQ9G4Jgo8cHyPc9Mzy/kZF64FOjmPfupXmvoCDKzBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:51:11.051483Z","bundle_sha256":"47ef06cb079ba8f454624ba197e7a11d41c435278d92d3f38a74dbd8a1329411"}}