{"id":"W4407287089","doi":"10.1007/s43670-025-00098-0","title":"The greedy side of the LASSO: new algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit","year":2025,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Matching pursuit; Lasso (programming language); Greedy algorithm; Algorithm; Computer science; Mathematics; Function (biology); Matching (statistics); Mathematical optimization; Compressed sensing; Statistics; Biology","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":[0.003109981,0.001805374,0.001539106,0.0009437784,0.0004897815,0.001467675,0.001974381,0.001987944,0.002008392],"category_scores_gemma":[0.007709956,0.0008814928,0.0009051511,0.001803071,0.001929626,0.003247513,0.003740438,0.004040399,0.001451467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004146358,"about_ca_system_score_gemma":0.0009771334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006645493,"about_ca_topic_score_gemma":0.0007335225,"domain_scores_codex":[0.9978111,0.0009141408,0.00008438209,0.0002613558,0.0008199871,0.0001090238],"domain_scores_gemma":[0.9981748,0.001006327,0.0001825844,0.0002808587,0.0002560657,0.00009947667],"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":[0.0005322177,0.0001795325,0.0005452469,0.0004176539,0.00021107,0.0001467766,0.0002061914,0.2153709,0.02182183,0.2421811,0.01986319,0.4985243],"study_design_scores_gemma":[0.00002947406,0.00008146249,0.00009460021,0.0000238425,0.0000140695,0.00008680929,0.00001584034,0.9337825,0.002778447,0.05868513,0.004379869,0.00002793919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009833912,0.0002428756,0.9978603,0.0001852756,0.00005986027,0.00001119673,0.00002595778,0.0001154002,0.0005157781],"genre_scores_gemma":[0.06421984,0.001403354,0.9272134,0.0005485335,0.0005120482,0.000205282,0.0002523015,0.0003774766,0.005267742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003109981,"threshold_uncertainty_score":0.01644731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961198463878358,"score_gpt":0.2807382421831374,"score_spread":0.2511262575443538,"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."}}