torch.pca_lowrank
torch.pca_lowrank(A, q=None, center=True, niter=2)
Performs linear Principal Component Analysis (PCA) on a low-rank
   matrix, batches of such matrices, or sparse matrix.
This function returns a namedtuple "(U, S, V)" which is the nearly
   optimal approximation of a singular value decomposition of a
   centered matrix A such that A = U diag(S) V^T.
Note:
 The relation of "(U, S, V)" to PCA is as follows:

 * A is a data matrix with "m" samples and "n" features

 * the V columns represent the principal directions

 * S ** 2 / (m - 1) contains the eigenvalues of A^T A / (m - 1)
   which is the covariance of "A" when "center=True" is provided.

 * "matmul(A, V[:, :k])" projects data to the first k principal
   components

Note:
 Different from the standard SVD, the size of returned matrices
 depend on the specified rank and q values as follows:

    * U is m x q matrix

    * S is q-vector
