{"id":"W2099609584","doi":"10.1109/igarss.2005.1525342","title":"Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)","year":2005,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Victoria","funders":"","keywords":"Hyperspectral imaging; Feature extraction; Nonlinear system; Embedding; Pattern recognition (psychology); Computer science; Artificial intelligence; Extraction (chemistry); Remote sensing; Geology; Physics; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002348766,0.0004450646,0.0004708671,0.000532469,0.0001724898,0.0004604033,0.0003131728,0.0002796838,0.001240794],"category_scores_gemma":[0.0009500884,0.0001842678,0.0003931513,0.0005768503,0.0002676213,0.0009654624,0.000498586,0.0005712155,0.0008020225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487277,"about_ca_system_score_gemma":0.0002309282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007166704,"about_ca_topic_score_gemma":0.001447791,"domain_scores_codex":[0.9998392,0.00003933402,0.00001053017,0.000032475,0.00006094617,0.00001755929],"domain_scores_gemma":[0.9996927,0.0001124235,0.00005225777,0.00004990239,0.00007706731,0.00001554427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001893235,0.0001404429,0.001234606,0.0002074613,0.00005077996,0.0001088302,0.0001103497,0.05358839,0.2232921,0.004951293,0.003311231,0.7128152],"study_design_scores_gemma":[0.00000907955,0.00006812702,0.002132197,0.00001349774,0.00001930566,0.0001160274,0.0000328947,0.9457827,0.04618708,0.003332879,0.002281226,0.00002505579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0268655,0.0001280675,0.9714225,0.0001099384,0.00001714061,0.00002193243,0.0001036399,0.0005778223,0.0007534749],"genre_scores_gemma":[0.4010156,0.0003245678,0.5930899,0.00008846026,0.00006035302,0.0001319059,0.0007015979,0.0002155135,0.004372194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001240794,"threshold_uncertainty_score":0.004150867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02833792529402468,"score_gpt":0.2927361114257568,"score_spread":0.2643981861317321,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}