How to use PCA (Principal component analysis) with SVM for classification in Mathlab?

The input data that I have is a matrix X (490*11) , where the rows of X correspond to observations and the 11 columns to correspond (predictors or variables). I need to apply the PCA on this matrix to choose a set of predictors (as a feature selection technique) .In Matlab, I know that I can use this function [coeff,score,latent]= pca(X) for applying the PCA on input matrix, but I don't know how to use the output of this function to create a new matrix that I need to use for training Support Vector Machine classifier. Please Help me!

[Pratyush]

I will answer this tomorrow

[the cyclist]

They've waited over four years for an answer, so I guess they can wait another day.