5. Tensor regression
TensorLy also allows you to perform Tensor Regression.
Tensor regression is available in the module
Given a series of tensor samples/observations, , and corresponding labels , we want to find the weight tensor such that, for each :
We additionally impose that be a rank-r CP decomposition (Kruskal regression) or a rank -Tucker decomposition (Tucker regression). For a detailed explanation on tensor regression, please refer to .
TensorLy implements both types of tensor regression as scikit-learn-like estimators.
For instance, Krusal regression is available through the
tensorly.regression.KruskalRegression object. This implements a fit method that takes as parameters X, the data tensor which first dimension is the number of samples, and y, the corresponding vector of labels.
Given a set of testing samples, you can use the predict method to obtain the corresponding predictions from the model.
|||W. Guo, I. Kotsia, and I. Patras. “Tensor Learning for Regression”, IEEE Transactions on Image Processing 21.2 (2012), pp. 816–827|