Data set used for Principal Component Analysis
Principal Component Analysis of Thermographic Data
... representative set of equally space responses for a single layer is shown in ...one set of responses with the first layer thickness being the same as used to create the “ single” layer response and ...
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Enhancements to a Geographically Weighted Principal Component Analysis in the Context of an Application to an Environmental Data Set
... other data, often have improved the accuracy of a given spatial classification study ...I data only reflect a univariate local spatial structure for each variable in ...to data preprocessing ...
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An incremental principal component analysis for chunk data
... There still remains several open questions. First, since the features are selected without considering the class sep- arability in IPCA, optimal features are not always ensured. To alleviate this problem, recently we ...
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PRINCIPAL COMPONENT ANALYSIS
... Second Analysis of the Investment Model Data The results obtained when item 11 was dropped from the analysis are very similar to those obtained when it was ...you used the ...
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Principal Component Analysis
... successive component accounts for a little ...few principal components in terms of the original variables, and thereby have a greater understanding of the ...
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Principal Component Analysis
... the data set such that the greatest variance of the data set comes to lie on the first axis (then called the principal component), the second greatest variance on the second ...
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Component retention in principal component analysis with application to cDNA microarray data
... microarray data sets Table 2 summarizes the results of the stopping criteria for six microarray data ...each data set was a major factor for all roots testing out to be significantly ...each ...
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Principal component gene set enrichment (PCGSE)
... genomic data in which important biological signals are defined by the collective action of groups of func- tionally related ...gene set testing methods have been widely applied in supervised settings to ...
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Group-Wise Principal Component Analysis for Exploratory Data Analysis
... active data visualization and analysis, and datasets for which the assumption of sparsity does not hold can be easily ...a set of nested PCA together with a suitable deflation pro- ...omics ...
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Functional principal component analysis of spatially correlated data
... forest data SPACE model is motivated by the spatial correlation observed in the Harvard Forest vegetation index data described in ...EVI data used in this work is extracted for a 25 pixel win- ...
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Streaming Principal Component Analysis From Incomplete Data
... A streaming PCA algorithm might also be interpreted as a stochastic algorithm for PCA (Arora et al., 2012). Stochastic projected gradient ascent in this context is closely related to the classical power method. In ...
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Compressive SAR raw data with principal component analysis
... raw data compressing based on CS theory, we can see that the SAR imagery data usually have poor sparsity feature and looking for suitable sparse transformation basis for SAR images is extremely ...and ...
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Principal component analysis on meteorological data in UTM KL
... meteorological data collected in Universiti Teknologi Malaysia Kuala ...the analysis, it was found that relative humidity has the most significant contribution on affecting solar ...
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Canonical Correlation Analysis of Principal Component Scores for Multiple-set Random Vectors
... each set and several ...each set, and MCCA is performed between PC scores in each ...scores used by ...be used for both of the covariance matrix and the correlation ...scores used for ...
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Principal component analysis of some parameters used for lycopene extraction from tomatoes
... V. B ARBU , C. N EAGU * and M. D RAGAN Food Science and Engineering Faculty, Dunarea de Jos University, 111 Domneasca Street, Galati. Romania (Received: 4 June 2014; accepted: 7 November 2014) The present research is on ...
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Interactive Principal Component Analysis
... Principal Component Analysis (PCA) is a method for find- ing projections of maximal variability in multidimensional ...a set of possibly correlated variables into a set of linearly ...
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Euler principal component analysis
... We used the complete set of preceding frames to train the models ...are used for the appearance model), and for each video, we eval- uate the similarity for the frames in which the ground truth is ...
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Functional principal component and factor analysis of spatially correlated data
... multivariate data interpretation of factors are primarily carried out based on the grouping of factor ...motivating data set for this paper consists of roughly weekly observations of vegetation ...
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Gene Expression Data Classification With Kernel Principal Component Analysis
... expression data is the classification of samples into different categories, such as the types of ...expression data are charac- terized by many variables on only a few ...the data variation. Prin- ...
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Properties of principal component methods for functional and longitudinal data analysis
... of principal component methods to analyze functional data is ap- propriate in a wide range of different ...“functional data analysis,” it has often been assumed that a sample of random ...
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