Non-negative Factorization of the Occurrence Tensor from Financial Contracts
This work addresses tensor factorization for financial document analysis, but it appears incremental as it adapts existing methods to a new dataset.
The authors tackled the problem of factorizing occurrence tensors from heterogeneous networks, specifically using financial contracts, by developing an efficient splitting algorithm to optimize a nonconvex and nonsmooth objective with l0 norm for sparse errors, achieving results on synthetic data and a new dataset called resMBS.
We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (occurrences), and use decomposed factors to model the embedded groups of nodes. An efficient splitting method is developed to optimize the nonconvex and nonsmooth objective. We study both synthetic problems and a new dataset built from financial documents, resMBS.