We propose a new online approach for multimodal dictionary learning. The method developed in this work addresses the great challenges posed by the computational resource constraints in dynamic environment when dealing with large scale tensor sequences. Given a sequence of tensors, i.e. a set composed of equal-size tensors, the approach proposed in this paper allows to infer a basis of latent factors that generate these tensors by sequentially processing a small number of data samples instead of using the whole sequence at once. Our technique is based on block coordinate descent, gradient descent and recursive computations of the gradient. A theoretical result is provided and numerical experiments on both real and synthetic data sets are performed.

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