{"id":"W2772226559","doi":"10.1109/jstars.2017.2775567","title":"Sparse Hyperspectral Unmixing via Heuristic $\\ell _p$ -Norm Approach","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Norm (philosophy); Heuristic; Computer science; Compressed sensing; Combinatorics; Algorithm; Mathematics; Artificial intelligence; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001168907,0.001346046,0.001028996,0.0009446227,0.0005334757,0.001054829,0.001813177,0.001438737,0.002912408],"category_scores_gemma":[0.002203808,0.000580553,0.001083014,0.001212724,0.001050004,0.001962134,0.001761627,0.001944338,0.00134088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006726281,"about_ca_system_score_gemma":0.001229141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959475,"about_ca_topic_score_gemma":0.003811791,"domain_scores_codex":[0.9992373,0.0002103961,0.00003210997,0.0001519422,0.0003102533,0.0000579574],"domain_scores_gemma":[0.999465,0.0002302669,0.00006451141,0.00006816005,0.0001406724,0.00003138322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001680338,0.0001191815,0.0004235294,0.000275471,0.000112508,0.0001174211,0.0001756789,0.6241974,0.0189892,0.04217205,0.004640853,0.3086087],"study_design_scores_gemma":[0.000006582427,0.00001698546,0.00004661041,0.000006507756,0.000006400585,0.00002512841,0.00001255298,0.9903203,0.002803473,0.005325953,0.001420257,0.000009192008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001452086,0.00007760044,0.9974126,0.0000704725,0.00001433454,0.00001715015,0.00001901623,0.0001806535,0.0007560616],"genre_scores_gemma":[0.05767747,0.0003191133,0.9377272,0.0002048764,0.00008028808,0.0001598807,0.000309484,0.0001993894,0.00332229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002959475,"threshold_uncertainty_score":0.009742975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056353078921072,"score_gpt":0.2322154179248178,"score_spread":0.2016518871356071,"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."}}