Eigen vector in SVD??

Im going to compute the eigen value and eigen vector from my Matrix data fro the classification.

The rows represent the different classes and the columns represent the features.

So, for example if I have

     X= 
      [2 3 4]  
      [3 2 4]   
      [4 5 6]  
      [8 9 0]

*I have to use SVD instead of PCA because the matrix is not square.*

What I have done are:

 1. Compute the mean for each row. So I have

        Mean=  
          M1  
          M2  
          M3  
          M4

 2. Substract my matrix **X** with the Mean

         Substract= 

        [2-M1 3-M1 4-M1]  
        [3-M2 2-M2 4-M2]   
        [4-M3 5-M3 6-M3]  
        [8-M4 9-M4 0-M4]

 3. Covariance Matrix = (Substract*Substract^t)/(4-1)

 4. [U,S,V] = svd(X)

Are all my step right? By computing the mean for each row (as the classes)?

If I want to project my data into eigen space (for dimensionality reduction), which is the eigen vector (U or V)??