{"id":"W2143885292","doi":"10.1109/tnn.2009.2031144","title":"Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Information Technology Research Centre; Universidade Federal do Rio de Janeiro; National Technical University of Athens; Concordia University; Royal Bank of Canada","keywords":"Multilinear map; Principal component analysis; Pattern recognition (psychology); Subspace topology; Linear subspace; Artificial intelligence; Projection (relational algebra); Tensor (intrinsic definition); Mathematics; Rank (graph theory); Computer science; Algorithm; Combinatorics","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":[0.001892778,0.00162431,0.00106953,0.001216274,0.0008446895,0.001241263,0.001109384,0.0008410749,0.003381661],"category_scores_gemma":[0.007503096,0.000498495,0.001334152,0.002485534,0.001229995,0.00157224,0.001448941,0.002488993,0.001904802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005973692,"about_ca_system_score_gemma":0.001571766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056163,"about_ca_topic_score_gemma":0.002520967,"domain_scores_codex":[0.9975408,0.001035888,0.0001288253,0.0004803425,0.0007128072,0.0001013841],"domain_scores_gemma":[0.997329,0.001219772,0.0002786338,0.0005426321,0.0005629333,0.0000669907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001503406,0.000133893,0.001480075,0.0005828994,0.0003459232,0.0002243365,0.0003065211,0.2463373,0.01968496,0.1243526,0.009516633,0.5968845],"study_design_scores_gemma":[0.00001833382,0.0000909645,0.0008901716,0.00004763525,0.00004890852,0.0001824422,0.00003799885,0.9047087,0.007822491,0.06761505,0.01847107,0.00006625646],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001126887,0.0002720831,0.9976215,0.00007207745,0.00003269457,0.00002702809,0.00005952509,0.0002167767,0.0005713839],"genre_scores_gemma":[0.06616212,0.0009003069,0.929785,0.0001319274,0.0001992961,0.0003174278,0.0006705019,0.0002224923,0.001611056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003381661,"threshold_uncertainty_score":0.01131278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163165835836361,"score_gpt":0.2362605338137632,"score_spread":0.2199439502301271,"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."}}