{"id":"W4382360363","doi":"10.1016/j.engappai.2023.106663","title":"Sparse representation learning using ℓ1−2 compressed sensing and rank-revealing QR factorization","year":2023,"lang":"lv","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Compressed sensing; Pattern recognition (psychology); Context (archaeology); Feature vector; Support vector machine; Feature (linguistics); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003656111,0.0003490913,0.0004325935,0.0006109779,0.0002587486,0.0001442718,0.0002227268,0.0002167105,0.00001561911],"category_scores_gemma":[0.0002375085,0.0004618364,0.00009946187,0.001693446,0.000102349,0.0002104086,0.000137046,0.0004361738,0.00005135111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008697376,"about_ca_system_score_gemma":0.0000309561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003859376,"about_ca_topic_score_gemma":0.000007076099,"domain_scores_codex":[0.9978303,0.00006307207,0.0008509195,0.000520363,0.0003063274,0.0004290642],"domain_scores_gemma":[0.9984513,0.0004366546,0.0002384106,0.0005096815,0.0002465347,0.0001174159],"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":[0.000007929421,0.00001425756,0.00006177257,0.0001109394,0.00004897521,0.000003027882,0.0006686794,0.7043856,0.2524036,0.00198353,0.00001726272,0.04029439],"study_design_scores_gemma":[0.00002490758,0.00001468776,0.00008345202,0.0002845084,0.00006233905,0.000006626539,0.0004397822,0.7261481,0.2712048,0.001102371,0.0003427192,0.0002856633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1160729,0.0003487042,0.8816104,0.0000417041,0.0002832388,0.0005483546,0.00001496791,0.0010058,0.00007391885],"genre_scores_gemma":[0.9666438,0.0005342116,0.03232177,0.000004019544,0.0002775187,0.00002125079,0.00006602748,0.0000991576,0.00003225143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8505709,"threshold_uncertainty_score":0.9997833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06094990806180935,"score_gpt":0.3069883736954304,"score_spread":0.2460384656336211,"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."}}