.png){kind=icon}
Return
A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2019.2928518.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering 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 general framework and its systematic algorithmic exploration are still lacking. In this paper, we first 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 in a very comprehensive manner. 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.
Keywords:
Sparse matrices
Pattern recognition
Data integration
Prediction algorithms
Data models
Matrix decomposition
Signal processing algorithms
Bioinformatics
data integration
network-regularized constraint
nonnegative matrix factorization (NMF)
pattern recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.9
Papers:
5.0K
Citations:
2.9W
Organization
Cited Papers
Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.png)
BMC GENOMICS
OAAI
BMC GENOMICSIF3.7
OAAIShareSaveShareSaveShareSaveAberrant expression of collagen triple helix repeat containing 1 in human solid cancersCLINICAL CANCER RESEARCH
CLINICAL CANCER RESEARCHIF10.2
PREAI
ShareSaveShareSaveShareSave
View more