{"id":"W1700224340","doi":"10.48550/arxiv.1409.4018","title":"EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hospital for Sick Children","keywords":"Non-negative matrix factorization; Cluster analysis; Computer science; Graph; Matrix decomposition; Range (aeronautics); Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Data set; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003271194,0.0004582667,0.0005493075,0.0003795453,0.0001935092,0.0001713385,0.002009191,0.0004043881,0.00002100345],"category_scores_gemma":[0.0001521115,0.0004989416,0.0003302023,0.0009695636,0.0001457409,0.0008898596,0.002124376,0.0006611175,0.000068179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002185044,"about_ca_system_score_gemma":0.0001288994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005858518,"about_ca_topic_score_gemma":0.000006733981,"domain_scores_codex":[0.9974772,0.0002851469,0.0003012351,0.001358889,0.0001573995,0.0004201096],"domain_scores_gemma":[0.9971679,0.0001890388,0.0005132195,0.001572044,0.0003782719,0.0001794663],"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":[0.000172983,0.0003916587,0.001645718,0.0007031557,0.0004175555,0.0005350157,0.0009464709,0.03754628,0.003463503,0.9310694,0.00101911,0.02208911],"study_design_scores_gemma":[0.001144412,0.0002250608,0.0006043236,0.0004750381,0.0001354083,0.0000066986,0.00003626335,0.3268569,0.01619168,0.6494153,0.003453224,0.001455727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005935803,0.0001255759,0.9910483,0.00006934767,0.0004350426,0.0005820314,0.00001685486,0.0009122544,0.0008747787],"genre_scores_gemma":[0.9522527,0.0006992371,0.04524918,0.00009118178,0.00008848077,0.00000273869,0.00003909,0.00003093423,0.00154642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.946317,"threshold_uncertainty_score":0.9997462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06292701533857968,"score_gpt":0.2310598876845958,"score_spread":0.1681328723460161,"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."}}