Results 31 to 40 of about 644,521 (264)
PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM [PDF]
Oradea Journal of Business and Economics, 2019 The impact of ignoring collinearity among predictors is well documented in a statistical literature. An attempt has been made in this study to document application of Principal components as remedial solution to this problem.
Abubakari S.Gwelo
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ANOVA bootstrapped principal components analysis for logistic regression
Croatian Review of Economic, Business and Social Statistics, 2022 Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal components is selected to be used in a classification or a regression model to boost accuracy.
Toleva Borislava
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Genetic associations between visual scores, body weight and age at first calving in nellore breed cattle [PDF]
Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2020 Body weight records of 231,416 Nellore females obtained from the Brazilian Association of Zebu Breeders were used to determine a linear combination between visual appraisal scores (body structure, precocity and muscling) using principal components ...
F.B. Freitas +5 more
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The Modified Principal Component Analysis Feature Extraction Method for the Task of Diagnosing Chronic Lymphocytic Leukemia Type B-CLL [PDF]
Journal of Universal Computer Science, 2020 The vast majority of medical problems are characterised by the relatively high spatial dimensionality of the task, which becomes problematic for many classic pattern recognition algorithms due to the well-known phenomenon of the curse of dimensionality ...
Mariusz Topolski
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Revista Ambiente & Água, 2021 “Erodibility” is a characteristic of the soil that represents the susceptibility with which its particles from the most superficial layer are taken and transported to lower places by erosive agents, causing environmental and economic damages.
Lucivânia Izidoro da Silva +6 more
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Principal components: A descent algorithm [PDF]
Journal of Computational Physics, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rebeca Salas-Boni, Esteban G. Tabak
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Principal Components Analysis Utility in the Livestock Field
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 2016 Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets.
Ancuta Simona Rotaru +3 more
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Dual Principal Component Pursuit [PDF]
2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015 We consider the problem of learning a linear subspace from data corrupted by outliers. Classical approaches are typically designed for the case in which the subspace dimension is small relative to the ambient dimension. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such, it is ...
Manolis C. Tsakiris, René Vidal
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Solid Pseudopapillary Neoplasm of the Pancreas in Children and Adolescents: Expert Recommendations
Pediatric Blood &Cancer, EarlyView.ABSTRACT Solid pseudopapillary neoplasm of the pancreas (SPN) is a rare low‐grade malignant exocrine pancreatic tumor, mostly discovered during the second decade of life in females, with a very good prognosis, provided microscopically complete surgical excision is achieved.
Sabine Irtan +18 more
wiley +1 more source
Linear Dimensionality Reduction: What Is Better?
DataThis research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’
Mohit Baliyan, Evgeny M. Mirkes
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