{"id":"W1964833136","doi":"10.1109/icdm.2012.85","title":"Adapting Component Analysis","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Computer science; Curse of dimensionality; Kernel (algebra); Test data; Kernel method; Representation (politics); Embedding; Artificial intelligence; Feature (linguistics); Feature vector; Independence (probability theory); Reproducing kernel Hilbert space; Machine learning; Algorithm; Data mining; Hilbert space; Mathematics; Support vector machine; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001467675,0.002493942,0.00172034,0.002855429,0.001015727,0.002104914,0.001847896,0.001317957,0.01259428],"category_scores_gemma":[0.006671655,0.0005458286,0.002143191,0.003512939,0.000737889,0.001772383,0.001934002,0.002028025,0.008669353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007266665,"about_ca_system_score_gemma":0.001573412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004553554,"about_ca_topic_score_gemma":0.003539443,"domain_scores_codex":[0.9979789,0.0004593325,0.0001158011,0.0005934924,0.000667506,0.0001850895],"domain_scores_gemma":[0.9981627,0.0004405006,0.00007791867,0.0003925355,0.0008565947,0.0000697488],"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":[0.0002319414,0.00012191,0.001352054,0.0003377396,0.0002699112,0.0001282881,0.0001301686,0.05875993,0.01272543,0.02953369,0.01862229,0.8777866],"study_design_scores_gemma":[0.00003471842,0.00008997502,0.003128269,0.00007200109,0.0001350493,0.0003197683,0.0001066333,0.8882645,0.01477025,0.03530489,0.0576711,0.0001028675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002996559,0.0005413044,0.9906641,0.0001223869,0.000249442,0.000166561,0.0002625325,0.001689899,0.003307194],"genre_scores_gemma":[0.1433904,0.001954316,0.8325692,0.0002734681,0.0004175685,0.0008293276,0.003304054,0.001333991,0.01592762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01259428,"threshold_uncertainty_score":0.04213208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577084472538145,"score_gpt":0.252069195281564,"score_spread":0.2262983505561825,"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."}}