A Unified Joint Matrix Factorization Framework for Data Integration
This work provides a unified framework for data integration and pattern recognition using NMF, which is incremental as it builds upon existing NMF variants.
The authors tackled the lack of a unified framework for analyzing multiple matrices simultaneously in nonnegative matrix factorization (NMF) by introducing a sparse multiple relationship data regularized joint matrix factorization (JMF) framework with two adapted prediction models and four update algorithms, demonstrating its effectiveness through extensive computational experiments on synthetic and real data.
Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering the hidden features and part-based patterns from high-dimensional data. NMF and its variants have been successfully applied into diverse fields such as pattern recognition, signal processing, data mining, bioinformatics and so on. Recently, NMF has been extended to analyze multiple matrices simultaneously. However, a unified framework is still lacking. In this paper, we introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration. Next, we present four update algorithms to solve this framework. The merits and demerits of these algorithms are systematically explored. Furthermore, extensive computational experiments using both synthetic data and real data demonstrate the effectiveness of JMF framework and related algorithms on pattern recognition and data mining.