Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IEEE Transactions on Signal Processing
Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
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Metabolomics focuses on the detection of chemical substances in biological fluids such as urine and blood using a number of analytical techniques including Nuclear Magnetic Resonance NMR spectroscopy and Liquid Chromatography-Mass Spectrometry LC-MS. Among the major challenges in analysis of metabolomics data are i joint analysis of data from multiple platforms, and ii capturing easily interpretable underlying patterns, which could be further utilized for biomarker discovery. In order to address these challenges, the authors formulate joint analysis of data from multiple platforms as a coupled matrix factorization problem with sparsity penalties on the factor matrices. They developed an all-at-once optimization algorithm, called CMF-SPOPT Coupled Matrix Factorization with SParse OPTimization, which is a gradient-based optimization approach solving for all factor matrices simultaneously. Using numerical experiments on simulated data, the authors demonstrate that CMF-SPOPT can capture the underlying sparse patterns in data. Furthermore, on a real data set of blood samples collected from a group of rats, the authors use the proposed approach to jointly analyze metabolomics data sets and identify potential biomarkers for apple intake. Advantages and limitations of the proposed approach are also discussed using illustrative examples on metabolomics data sets.