Work

Non-negative Factorization of the Occurrence Tensor from Financial Contracts

Year: 2016

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1612.03350

Authors: Xu, Zheng, Huang, Furong, Raschid, Louiqa, Goldstein, Tom

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2565638635

Cites: 19

Cited by: 1

Related to: 10