want to find eigenvalues/eigenvectors of the
covariance matrix XTX. So my workaround is to find singular values and
right singular vector of X in order to use the following equivalency.
Comparison with the eigenvector factorisation of *X*T*X* establishes that
the right singular vectors *W* of *X
eigenvalues/eigenvectors of the
covariance matrix XTX. So my workaround is to find singular values and
right singular vector of X in order to use the following equivalency.
Comparison with the eigenvector factorisation of *X*T*X* establishes that
the right singular vectors *W* of *X
vectors
eigenMap.put(singularValues[i] * singularValues[i], (DenseVector)
eigenVectors.viewRow(i));
}
return eigenMap;
}
In case my problem is unclear, here's some context,
I have an input matrix X and I want to find eigenvalues/eigenvectors
Hi,
I am currently working with SingularValueDecomposition class and I like to
clarify the following.
My goal is to find eigenvalues and corresponding eigenvectors of a matrix.
I know how to calculate eigenvalues and eigenvectors using svd but is there
a way to keep track of which eigenvector
The order of the singular values and vectors should tell you.
For others who might be curious, the singular value decomposition breaks a
matrix A into three factors
A = U S V'
Both U and V are orthonormal so that U' U = I and V' V = I. S is diagonal.
An eigenvalue decomposition decomposes
