This is exactly what PCA aims.
CCA, on the other hand, isn't aware of classes and process the data as if they all were continuous variables - which is more general but a slower way of computation.
So, principal components maximize variance and discriminants maximize class separation; a simple case where a PC fails to discriminate between classes well enough but a discriminant can is these pictures.
Dimensionality reduction algorithms, before go straight ahead to code, lets talk about dimensionality reduction algorithms.Pour les salariés, cette solution va à lencontre de leurs attentes car ils craignent que le placement en procédure judiciaire se traduise par un démantèlement, voire une disparition de leur entreprise, ce qui engendrera la suppression de leurs emplois.Indeed, it is a pretty good number, it means that there is just 2 of information idee cadeau noel fille 14 ans being lost.The confusion matrix shows really good results this time, the neural network is committing less misclassification in both classes, it can be seen though the values of the main diagonal and also the accuracy value is around.Other results remain valid.
There are two principal algorithms for dimensionality reduction: Linear Discriminant Analysis ( LDA ) and Principal Component Analysis ( PCA ).
This topic is definitively one of the most interesting ones, it is great to think that there are algorithms able to reduce the number of features by choosing the most important ones that still represent the entire dataset.
To repeate, this is actually CCA in its nature.
At dimensionality reduction we extract discriminant functions which replace the original explanatory variables.
The dataset is originated from UCI machine learning repository called Statlog ( Vehicle Silhouettes ) dataset.This dataset stores some measures of four vehicless silhouettes with the purpose of classification.Basically, confusion matrix says how much examples were classified into classes.Percentage of Variance from each Principal Component # Scree Plot plot( cumsum( prop_varex xlab "Principal Component ylab "Cumulative Proportion of Variance Explained type "b" ) Figure.Les positions des acteurs internes semblent inconciliables car les salariés attendent des solutions et souhaitent que de nouveaux emprunts soient réalisés pour maintenir lemploi, tandis que lactionnaire principal et lÉtat attendent que la société soit placée en procédure judiciaire pour apurer les comptes qui sont.