Results 31 to 40 of about 597,726 (265)
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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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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Principal Component Regression by Principal Component Selection
Communications for Statistical Applications and Methods, 2015 Abstract We propose a selection procedure of principal components in principal component regression. Our methodselects principal components using variable selection procedures instead of a small subset of major principalcomponents in principal component regression. Our procedure consists of two steps to improve estimation andprediction.
Hosung Lee, Yun Mi Park, Seokho Lee
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Principal Component Classification
CoRR, 2022 We propose to directly compute classification estimates by learning features encoded with their class scores using PCA. Our resulting model has a encoder-decoder structure suitable for supervised learning, it is computationally efficient and performs well for classification on several datasets.
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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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Euler Principal Component Analysis [PDF]
International Journal of Computer Vision, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stephan Liwicki +3 more
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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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Dynamic Functional Principal Components [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014 SummaryWe address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such functional time series frequently arise, for example, when a continuous time process is segmented into some smaller natural units, such as days. Then each X t represents one intraday curve.
Hörmann, Siegfried +2 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Cup‐like nuclei are a distinctive morphological feature observed in certain cases of acute lymphoblastic leukemia (ALL). We provide evidence that they characterize DUX4/ERG ALL independently of IKZF1 deletion and reveal marked mitochondrial accumulation in this ALL subset.
Chloé Arfeuille +9 more
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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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