{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002914421,0.0000733507,0.00005925123,0.00005374968,0.00006015322,0.00004756531,0.0002235062,0.00003209141,0.0002104778],"category_scores_gemma":[0.000003883709,0.00005872724,0.00003138604,0.0001286729,0.00001033646,0.0002005477,0.0001648646,0.00004609576,0.0004018768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079848,"about_ca_system_score_gemma":0.0000103725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000502871,"about_ca_topic_score_gemma":0.00001180779,"domain_scores_codex":[0.9992008,0.00001193884,0.0001293987,0.0001929352,0.0002586453,0.0002063148],"domain_scores_gemma":[0.999624,0.00002140894,0.00003000178,0.0001958259,0.00002824441,0.0001004995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005455076,0.0001994056,0.000139478,0.000004231837,0.00001542279,0.00004837256,0.000360645,0.000007899955,0.03775593,0.005639754,0.03015521,0.9256682],"study_design_scores_gemma":[0.0005791332,0.0001677801,0.000843573,0.00002400285,0.000004158927,0.00002079669,0.0002662263,0.02348544,0.9033738,0.001234894,0.06974623,0.0002539474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01145628,0.00004547515,0.9732499,0.001131569,0.0003231248,0.00009692059,0.000001207762,0.0001320776,0.01356343],"genre_scores_gemma":[0.3072347,0.00003550729,0.6871086,0.002412344,0.0001785264,0.000008275269,0.000008495041,0.000007333645,0.003006112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9254143,"threshold_uncertainty_score":0.5165447,"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."}}