{"id":"W2126175004","doi":"10.1109/iai.2004.1300936","title":"A comparison of subspace methods for accurate position measurement","year":2004,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Principal component analysis; Kernel principal component analysis; Subspace topology; Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Linear subspace; Kernel (algebra); Independent component analysis; Computer science; Position (finance); Kernel Fisher discriminant analysis; Mathematics; Computer vision; Kernel method; Support vector machine; Facial recognition system","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":[0.002724612,0.001257831,0.001140955,0.003220139,0.0005497799,0.001457847,0.0008563852,0.0009959684,0.00285294],"category_scores_gemma":[0.01094324,0.0004617621,0.0006737448,0.002869727,0.0004891906,0.00165059,0.001183121,0.0007057684,0.001979434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004019084,"about_ca_system_score_gemma":0.0007042774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001864647,"about_ca_topic_score_gemma":0.001998217,"domain_scores_codex":[0.9959813,0.00117027,0.0001637059,0.0003830375,0.002157542,0.0001441093],"domain_scores_gemma":[0.9933618,0.00292823,0.0003708883,0.0008801288,0.002335677,0.0001231897],"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":[0.0005652527,0.00007013191,0.001821926,0.0003811535,0.000191767,0.00004592714,0.0001330875,0.03758297,0.02563092,0.005803211,0.002187666,0.9255861],"study_design_scores_gemma":[0.0001042026,0.000893703,0.01083034,0.0001918177,0.0001386705,0.001296965,0.0002983751,0.8807941,0.0766286,0.01122195,0.01735489,0.0002463559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01761102,0.002012204,0.9751902,0.0000866659,0.00008668541,0.00006296786,0.0001415794,0.00226334,0.002545394],"genre_scores_gemma":[0.1912389,0.002225948,0.8034956,0.00005925502,0.0000710251,0.0001451934,0.0006238061,0.0003850152,0.001755282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003220139,"threshold_uncertainty_score":0.0144093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1431735117420525,"score_gpt":0.4646293531960592,"score_spread":0.3214558414540066,"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."}}