{"id":"W2886643713","doi":"10.1186/s13673-018-0148-3","title":"Improving clustering performance using independent component analysis and unsupervised feature learning","year":2018,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; MNIST database; Pattern recognition (psychology); Independent component analysis; Artificial intelligence; Computer science; Principal component analysis; Unsupervised learning; Feature extraction; Feature (linguistics); Correlation clustering; Deep learning","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00156037,0.0001437345,0.0001847002,0.0007948183,0.001785073,0.001275671,0.0003813221,0.0000599167,0.000004525987],"category_scores_gemma":[0.0000399191,0.0001284275,0.00003662264,0.001568511,0.000210895,0.003290584,0.0005363558,0.0001952539,0.000003200773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004085936,"about_ca_system_score_gemma":0.00004869039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183082,"about_ca_topic_score_gemma":0.000009227803,"domain_scores_codex":[0.9985558,0.00009809472,0.0003540912,0.0002799826,0.0004314035,0.0002806354],"domain_scores_gemma":[0.9992043,0.00006491309,0.0003134334,0.0001601948,0.0001710558,0.00008606874],"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":[0.00001779752,0.00007245948,0.1614063,0.0002409413,0.0001578035,0.000002216006,0.05586439,0.05065736,0.002515799,0.0148686,0.00007356983,0.7141227],"study_design_scores_gemma":[0.0001970947,0.0001094724,0.03897075,0.00002601202,0.0000171749,0.00001766635,0.000340708,0.9593539,0.0004593874,0.00002187452,0.0003240784,0.0001619137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5115255,0.0000341651,0.4876888,0.00006097117,0.00005061626,0.00007433855,1.827111e-7,0.0001324799,0.0004330054],"genre_scores_gemma":[0.96362,0.00002249833,0.03603007,0.0002675666,0.00004217185,9.668479e-7,0.000003017015,0.000002401893,0.00001130341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9086965,"threshold_uncertainty_score":0.9997611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347743665580084,"score_gpt":0.2838489163022337,"score_spread":0.2603714796464329,"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."}}