Abstract
Given data from a variety of sources that share a number of dimensions, how can we effectively decompose them jointly into interpretable latent factors? The coupled tensor decomposition framework captures this idea by jointly supporting the decomposition of several CP tensors. However, coupling tends to suffer when one dimension of data is irregular, i.e., one of the dimensions of the tensor is uneven, such as in the case of PARAFAC2. In this work, we provide a scalable method for decomposing coupled CP and PARAFAC2 tensor datasets through non-negativity-constrained least squares optimization on a variety of objective functions. Comprehensive experiments on large data confirmed that C3APTION is up to 5× faster and 70 − 80% accurate than several baselines.
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