384 search results for "PCA"

Finding a pin in a haystack – PCA image filtering

December 4, 2012
By
Finding a pin in a haystack – PCA image filtering

I found the following post regarding the anomalous metal object observed in a Curiosity Rover photo to be fascinating - specifically, the clever ways that some programmers used for filtering the image for the object. The following answer on mathematica.stackexchange.com was especially illuminating for its use of a multivariate distribution to...

Read more »

Looking to the PCA scores with GGobi

October 21, 2012
By
Looking to the PCA scores with GGobi

In this post I continue with the unsupervised exploration of oil spectra, which we have seen in previous post ( PCA with "ChemoSpec" - 001).In the manual "ChemoSpec:An R Package for Chemometric Analysis of Spectroscopic Data", (page 23) there is a brie...

Read more »

PCA with "ChemoSpec" – 001

October 20, 2012
By
PCA with "ChemoSpec" – 001

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...

Read more »

PCA or Polluting your Clever Analysis

August 31, 2012
By
PCA or Polluting your Clever Analysis

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,...

Read more »

PCA and ggplot2 to recognise gestures (via David…

June 12, 2012
By
PCA and ggplot2 to recognise gestures (via David…

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

Read more »

PCA for NIR Spectra_part 006: "Mahalanobis"

February 28, 2012
By
PCA for NIR Spectra_part 006: "Mahalanobis"

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 ...

Read more »

PCA for NIR Spectra_part 005: "Reconstruction"

February 27, 2012
By
PCA for NIR Spectra_part 005: "Reconstruction"

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...

Read more »

PCA for NIR Spectra_part 004: "Projections"

February 26, 2012
By
PCA for NIR Spectra_part 004: "Projections"

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...

Read more »

PCA for NIR Spectra_part 003: "NIPALS"

February 25, 2012
By
PCA for NIR Spectra_part 003: "NIPALS"

> 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...

Read more »

PCA for NIR Spectra_part 002: "Score planes"

February 23, 2012
By
PCA for NIR Spectra_part 002: "Score planes"

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...

Read more »

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)