# Which eigenvector to choose after calculating PCA

After calculating the principal component analysis (PCA) of a given data set, we are normally left with a matrix containing the eigenvectors sorted in order of the size of the eigenvalues. Now, in pattern recognition which eigenvector should we choose: the first eigenvector or do we have to do further processing of the eigenvector in order to choose the desired eigenvector?

• Could you proive me (and further readers) with an explanation what PCA stands for? – Deve Jul 12 '13 at 6:31
• – endolith Jul 12 '13 at 13:46