robust_pca(X, mask=None, tol=1e-06, reg_E=1.0, reg_J=1.0, mu_init=0.0001, mu_max=10000000000.0, learning_rate=1.1, n_iter_max=100, return_errors=False, verbose=1)[source]

Robust Tensor PCA via ALM with support for missing values

Decomposes a tensor X into the sum of a low-rank component D and a sparse component E.


tensor data of shape (n_samples, N1, …, NS)


array of booleans with the same shape as X should be zero where the values are missing and 1 everywhere else


convergence value

reg_Efloat, optional, default is 1

regularisation on the sparse part E

reg_Jfloat, optional, default is 1

regularisation on the low rank part D

mu_initfloat, optional, default is 10e-5

initial value for mu

mu_maxfloat, optional, default is 10e9

maximal value for mu

learning_ratefloat, optional, default is 1.1

percentage increase of mu at each iteration

n_iter_maxint, optional, default is 100

maximum number of iteration

return_errorsbool, default is False

if True, additionally returns the reconstruction errors

verboseint, default is 1

level of verbosity

(D, E) or (D, E, rec_errors)

Robust decomposition of X

DX-like array

low-rank part

EX-like array

sparse error part

rec_errorslist of errors

only returned if return_errors is True


The problem we solve is, for an input tensor \(\tilde X\):

\begin{equation*} \begin{aligned} & \min_{\{J_i\}, \tilde D, \tilde E} & & \sum_{i=1}^N \text{reg}_J \|J_i\|_* + \text{reg}_E \|E\|_1 \\ & \text{subject to} & & \tilde X = \tilde A + \tilde E \\ & & & A_{[i]} = J_i, \text{ for each } i \in \{1, 2, \cdots, N\}\\ \end{aligned} \end{equation*}