{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002427016,0.002158269,0.001970669,0.002591542,0.0007652463,0.002113198,0.001162373,0.00197666,0.006071983],"category_scores_gemma":[0.006053891,0.0005822767,0.001366204,0.005670881,0.002539902,0.003018049,0.001451287,0.004436374,0.003646032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115609,"about_ca_system_score_gemma":0.0011989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002302747,"about_ca_topic_score_gemma":0.001644382,"domain_scores_codex":[0.9976694,0.0008178456,0.0001757271,0.0004329811,0.0007924769,0.000111502],"domain_scores_gemma":[0.9969117,0.002087243,0.0002458248,0.0002687885,0.0004299248,0.00005639618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006846142,0.00008157972,0.0007657586,0.002407335,0.0001629932,0.0003381871,0.0004684325,0.04163079,0.004654618,0.5272501,0.03680014,0.3853716],"study_design_scores_gemma":[0.00003630677,0.0001680977,0.001142273,0.0006261911,0.00007595025,0.0007772018,0.0001564981,0.1923312,0.003269428,0.6370622,0.1642252,0.0001295978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001337707,0.05137513,0.9340898,0.00232981,0.001144919,0.00009386671,0.000315045,0.0003644641,0.008949256],"genre_scores_gemma":[0.07928798,0.1610388,0.7386453,0.001573349,0.007243889,0.0006645024,0.00110757,0.0002263542,0.01021232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006071983,"threshold_uncertainty_score":0.02031279,"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."}}