Randomized Algorithms For Low-Rank Tensor Decompositions In The Tucker Format

Many applications in data science and scientific computing involve large-scale datasets that are expensive to store and manipulate. However, these datasets possess inherent multidimensional structure that can be exploited to compress and store the dataset in an appropriate tensor format. In recent years, randomized matrix methods have been used to efficiently and accurately compute low-rank matrix decompositions. Motivated by this success, we develop randomized algorithms for tensor decompositions in the Tucker representation. Specifically, we present randomized versions of two well-known compression algorithms, namely, HOSVD and STHOSVD, and a detailed probabilistic analysis of the error in using both algorithms. We also develop variants of these algorithms that tackle specific challenges posed by large-scale datasets. The first variant adaptively finds a low-rank representation satisfying a given tolerance, and it is beneficial when the target rank is not known in advance. The second variant preserves the structure of the original tensor and is beneficial for large sparse tensors that are difficult to load in memory. We consider several different datasets for our numerical experiments: synthetic test tensors and realistic applications such as the compression of facial image samples in the Olivetti database and word counts in the Enron email dataset.