{"id":"W2967468293","doi":"10.1007/978-3-030-27202-9_7","title":"Principal Component Analysis Using Structural Similarity Index for Images","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subspace topology; Principal component analysis; Pattern recognition (psychology); Kernel principal component analysis; Euclidean distance; Kernel (algebra); Similarity (geometry); Image (mathematics); Fidelity; Component (thermodynamics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00121902,0.0006404319,0.0009967181,0.00132623,0.0003884764,0.001029027,0.003440263,0.0003317543,0.00002135766],"category_scores_gemma":[0.00006345076,0.0005846013,0.0004707877,0.0009711633,0.0004989367,0.0009430483,0.001878993,0.0007357651,0.000008258898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578892,"about_ca_system_score_gemma":0.0009425475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009857769,"about_ca_topic_score_gemma":0.0001083941,"domain_scores_codex":[0.9951453,0.0000676639,0.0007530096,0.001906503,0.001260938,0.000866622],"domain_scores_gemma":[0.9964211,0.000608208,0.0005050944,0.001836897,0.0004475475,0.0001811531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004460356,0.00009136526,0.002512431,0.000357953,0.000558843,0.0001096082,0.001745805,0.7040269,0.001118371,0.03967389,0.00002817554,0.2497321],"study_design_scores_gemma":[0.0003575304,0.0001054539,0.002167828,0.00009511954,0.0001042769,0.00001877841,3.246663e-7,0.957659,0.001089839,0.03736945,0.0002950568,0.0007373082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001081746,0.0001658664,0.9956523,0.0004397123,0.001420423,0.0007766391,0.00003673578,0.0001078202,0.0003187323],"genre_scores_gemma":[0.4013699,0.000006699568,0.5967515,0.001261997,0.0003486385,0.000006815905,0.00002313896,0.00002987305,0.0002015158],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4002881,"threshold_uncertainty_score":0.9996606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03915807744217148,"score_gpt":0.3255338781698875,"score_spread":0.2863758007277161,"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."}}