{"id":"W2147461642","doi":"10.1109/ccece.2007.300","title":"Incremental Line Tangent Space Alignment Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Embedding; Intrinsic dimension; Hessian matrix; Dimension (graph theory); Tangent space; Linear subspace; Curse of dimensionality; Projection (relational algebra); Nonlinear dimensionality reduction; Manifold (fluid mechanics); Tangent; Line (geometry); Algorithm; Representation (politics); Space (punctuation); Mathematics; Basis (linear algebra); Computer science; Parallelizable manifold; Dimensionality reduction; Artificial intelligence; Applied mathematics; Geometry; Combinatorics","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":[0.0006141467,0.001015482,0.001340895,0.001600026,0.0007469901,0.001450469,0.002288449,0.0009331106,0.008646131],"category_scores_gemma":[0.002237865,0.0005338605,0.0008099801,0.00159243,0.000544794,0.002450705,0.00161937,0.001341171,0.00479276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005413136,"about_ca_system_score_gemma":0.0009871757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002872286,"about_ca_topic_score_gemma":0.00291434,"domain_scores_codex":[0.9989524,0.0001581673,0.0000572356,0.0003353973,0.0003894751,0.0001071688],"domain_scores_gemma":[0.9990265,0.0001403067,0.0000812742,0.0002602875,0.0004249534,0.00006675609],"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":[0.0002744442,0.0001636362,0.001431975,0.0001039413,0.00009603766,0.0001982519,0.0001490334,0.05929794,0.0199141,0.01133146,0.01352259,0.8935166],"study_design_scores_gemma":[0.00004173248,0.0002272127,0.0008715309,0.00001273547,0.00003531994,0.0003710347,0.00008505951,0.9560655,0.01900892,0.008731822,0.01450185,0.00004726398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007572133,0.0001301306,0.9872214,0.00005870546,0.00007231659,0.00006856278,0.00009705931,0.003607321,0.001172387],"genre_scores_gemma":[0.1935189,0.0001726157,0.7961847,0.0001481449,0.00008959456,0.000228932,0.001265024,0.000656481,0.007735605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008646131,"threshold_uncertainty_score":0.02892417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608505589031123,"score_gpt":0.2592449696904536,"score_spread":0.2431599138001424,"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."}}