... robust **principal** **component** **analysis** is to perform dimension reduction, and to ﬁnd an optimal subspace of a certain dimension, then trimmed PCA is a natural ...the **principal** **component** ...

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... performance **analysis** and anomaly detection (PAAD) which can detect anomalies and point out the origin of the detected ...applies **principal** **component** **analysis** (PCA) technique to uncover the ...

8

... and **Principal** **Component** **Analysis** (PCA) in order to extract all those terms that are more relevant in a web site marked in a social bookmarking ...

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... The best holistic classification results are based on using Support Vector Machine (SVM) as classifiers [Moghaddam and Yang, 2002; Andreu and Mollineda, 2008; Mäkinen and Raisamo, 2008]. Moghaddam and Yang [Moghaddam and ...

9

... called **Principal** **Component** **Analysis** (PCA), allows for a sensitive and quick **analysis** of the inflows into a DMA without hassling mathematical ...

21

... of **principal** **component** **analysis** (PCA) is to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while retaining as much as possible of the variation present ...

12

... Through **principal** **component** **analysis** (PCA) the 2 sets of original variables were transformed into 4 different sets to produce the species distribution models with the modeling application in ...

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... a **principal** **component** **analysis**, then 2) individual forecasts on the most significant components or eigenvectors are calculated using a neural networks - wavelet decomposition ...

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... kernel **principal** **component** **analysis** using the Carnegie Mellon University database as a testing ...with **principal** **component** **analysis** and discussed where the percentages of sucess ...

6

... of **Principal** **Component** **Analysis** and Factor ...the **Principal** **Component** **Analysis** and the Maximum Likelihood Factor **Analysis** ...

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... This paper presents an application of Functional **Principal** **Component** **Analysis** (FPCA) to describe inter-subject variability of multiple waveforms. This technique was applied to the study of ...

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... We use a combination of nearly 30 years of monthly means of temperature and precipitation data from 173 meteorological stations to carry out a climate classification of the state of San Luis Potosí, México. PCA ...

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... The simulation considers a single bus – load equivalent and controlled variation of the equivalent system impedance behind the load. Variations of data structure are proposed and analyzed in order to interpret results ...

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... second **component** can make the bump at the end of the rising branch ( around phase ...third **component** is important to reproduce the double-peaked light curves displayed by some of the ...

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... Proximate composition of six food spices commonly used in South-East Nigeria are classified by **principal** **component** **analysis** (PCAs) of constituents and spices cluster **analysis** (CAs). Samples ...

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... by **Principal** **Component** **Analysis** (PCA) and Canonical Variate **Analysis** (CVA) allowed the identification of the finishing treatment used on the studied leather ...

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... 5.9 Diagnostic result for emulated faults in the actuators (cases [1 2]) 50 5.10 Diagnostic result for emulated faults in the actuators (cases [3 4]) 51 5.11 Fault detection result[r] ...

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... The main aim of the present report is to develop code learning potentialities and, since molecules are more naturally described via varying size structured representation, the study of general approaches to ...

12

... by **principal** **component** **analysis** (PCA) and consequent orthogonal partial least squares discrimination **analysis** (OPLS-DA) using fatty acid (FA) proﬁle differ- ences is ...Multivariate ...

5

... approaches used in wine metabolomics studies are principal component analysis PCA.. is based on dimension reduction and is often used as a preprocessing step prior to the.[r] ...

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