Results 31 to 40 of about 2,337,506 (337)
Properties of Design-Based Functional Principal Components Analysis [PDF]
, 2009 This work aims at performing Functional Principal Components Analysis (FPCA) with Horvitz-Thompson estimators when the observations are curves collected with survey sampling techniques.
Benko +37 more
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Sparse logistic principal components analysis for binary data [PDF]
, 2010 We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua +2 more
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Recursive principal components analysis [PDF]
Neural Networks, 2005 A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by
openaire +3 more sources
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
doaj +3 more sources
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
doaj +1 more source
Histogram of Oriented Principal Components for Cross-View Action Recognition [PDF]
, 2015 Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown
Huynh, Du +3 more
core +1 more source
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
doaj
Functional principal components analysis via penalized rank one approximation [PDF]
, 2008 Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways.
Buja, Andreas +2 more
core +5 more sources
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 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
doaj +1 more source