{"id":"W4380484875","doi":"10.3390/math11122674","title":"Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Matrix decomposition; Sparse matrix; Non-negative matrix factorization; Computer science; Sparse approximation; Pattern recognition (psychology); Matrix (chemical analysis); K-SVD; Artificial intelligence; Compressed sensing; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001021682,0.0001084378,0.0001359928,0.00015449,0.00003417157,0.00004553288,0.00005660391,0.00006152246,0.00001398534],"category_scores_gemma":[0.00002390553,0.000105766,0.000009555608,0.0002243142,0.00001961448,0.00005566688,0.00003076494,0.0001388008,0.00001119674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002007334,"about_ca_system_score_gemma":0.000005787449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006312999,"about_ca_topic_score_gemma":0.000007403495,"domain_scores_codex":[0.9994802,0.00001076921,0.0001757987,0.00008541402,0.0001063657,0.0001414871],"domain_scores_gemma":[0.9997962,0.00004528544,0.00003407701,0.00007444885,0.00002546073,0.00002450799],"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":[0.00002550792,0.0004066167,0.03163532,0.006057908,0.0001453239,0.0004174307,0.02339849,0.02906014,0.1681352,0.03651627,0.03627335,0.6679285],"study_design_scores_gemma":[0.00006908924,0.00002923639,0.0002879079,0.0001663771,0.000008326112,0.000007821565,0.00007560566,0.9612162,0.01859514,0.01654515,0.002827064,0.0001721117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04585577,0.0002674852,0.94851,0.00002036771,0.00003277071,0.00024693,0.00001820222,0.003471937,0.001576527],"genre_scores_gemma":[0.8904623,0.0002881627,0.1088255,0.00000601985,0.00002825085,0.00001629679,0.00003591814,0.0000506229,0.0002869945],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.932156,"threshold_uncertainty_score":0.4313012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594817050943669,"score_gpt":0.2604353705775595,"score_spread":0.2444872000681228,"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."}}