352 search results for "PCA"

PCA with "ChemoSpec" – 001

October 20, 2012
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In my last post about "ChemoSpec package" (Hierarchical Cluster Analysis (ChemoSpec) - 02), we saw the two cluster groups (one for olive oil, other for sunflower oil), and also another sub-clusters for the sunflower oil.Continue reading the manual "Che...

PCA or Polluting your Clever Analysis

August 31, 2012
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When I learned about principal component analysis (PCA), I thought it would be really useful in big data analysis, but that's not true if you want to do prediction. I tried PCA in my first competition at kaggle, but it delivered bad results. This post illustrates how PCA can pollute good predictors.When I started examining this problem,...

PCA and ggplot2 to recognise gestures (via David…

June 12, 2012
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PCA and ggplot2 to recognise gestures (via David Chudzicki’s Blog: Visualizing ChaLearn Gestures Test Data)

PCA for NIR Spectra_part 006: "Mahalanobis"

February 28, 2012
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Outliers have an important influence over the PCs, for this reason they must be detected and examinee.We have just the spectra without lab data, and we have to check if any of the sample spectra is an outlier ( a noisy spectrum, a sample which belongs ...

PCA for NIR Spectra_part 005: "Reconstruction"

February 27, 2012
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We saw how to plot the raw spectra (X), how to calculate the mean spectrum, how to center the sprectra (subtracting the mean spectrum from every spectra of the original matrix X). After that we have developed the PCAs with the NIPALS algorithm, getting...

PCA for NIR Spectra_part 004: "Projections"

February 26, 2012
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This plot in 2D, help us to decide the number of PCs, it is easy to create in R, once we have discompose the X matrix into a P matrix (loadings) and a T matrix (scores).For this plot, we just need the T matrix.> CPs<-seq(1,10,by=1)>  matp...

PCA for NIR Spectra_part 003: "NIPALS"

February 25, 2012
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> X<-yarn\$NIR> X_nipals<-nipals(X,a=10,it=100)Two matrices are generated (P and T)As in other posts, we are going to look to the loadings & scores, for firsts three principal components:> wavelengths<-seq(1,268,by=1)> matplot(w...

PCA for NIR Spectra_part 002: "Score planes"

February 23, 2012
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The idea of this post is to compare the score plots for the first 3 principal components obtained with the algorithm “svd” with the scores plot of  other chemometric software (Win ISI in this case). Previously I had exported the yarn spectra t...

February 22, 2012
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There are different algorithms to calculate the Principal Components (PCs). Kurt Varmuza & Peter Filzmozer explain  them in their book: “Introduction to Multivariate Statistical Analysis in Chemometrics”.I´m going to apply one of them, to...

Modelling returns using PCA : Evidence from Indian equity market

December 26, 2011
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As my finance term paper, I investigated an interesting question where I tried to identify macroeconomic variables that explain the returns on equities. Much of the debate has already taken place on this topic which has given rise to two competing theo...