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Tensor decompositions are invaluable tools in analyzing multimodal datasets.In many real-world scenarios, such datasets are far from being static, to thecontrary they tend to grow over time. For instance, in an online social networksetting, as we observe new interactions over time, our dataset gets updated inits "time" mode. How can we maintain a valid and accurate tensor decompositionof such a dynamically evolving multimodal dataset, without having to re-computethe entire decomposition after every single update? In this paper we introduceSaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm,which incrementally maintains the decomposition given new updates to the tensordataset. SaMbaTen is able to scale to datasets that the state-of-the-art inincremental tensor decomposition is unable to operate on, due to its ability toeffectively summarize the existing tensor and the incoming updates, and performall computations in the reduced summary space. We extensively evaluate SaMbaTenusing synthetic and real datasets. Indicatively, SaMbaTen achieves comparableaccuracy to state-of-the-art incremental and non-incremental techniques, whilebeing 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse anddense dynamically evolving tensors of dimensions up to 100K x 100K x 100K wherestate-of-the-art incremental approaches were not able to operate.

SamBaTen: Sampling-based Batch Incremental Tensor Decomposition