# Data Fusion by Matrix Factorization

arXiv: https://arxiv.org/abs/1307.0803 · HTML: https://mlsanity.com/paper/1307.0803/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2013.

## Built on by (most cited first, 7 of 7)

- [FastSTMF: Efficient tropical matrix factorization algorithm for sparse data](https://mlsanity.com/paper/2205.06619/) (2022)
- [Matrix tri-factorization over the tropical semiring](https://mlsanity.com/paper/2305.06624/) (2023)
- [Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization](https://mlsanity.com/paper/2109.13164/) (2021)
- [MetaMIML: Meta Multi-Instance Multi-Label Learning](https://mlsanity.com/paper/2111.04112/) (2021)
- [Multimodal Representation Learning With Text and Images](https://mlsanity.com/paper/2205.00142/) (2022)
- [HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction](https://mlsanity.com/paper/2312.00189/) (2023)
- [PathFinder: Joint Decompositions of Linked Multimodal Datasets](https://mlsanity.com/paper/2608.14951/) (2026)

## Abstract (arXiv)

For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system's constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.