Name
Abstract | ||
|---|---|---|
Nonnegative matrix tri-factorization (NMTF) X ≈ FSGT with all matrices nonnegative can reveal simultaneous row and column clusters of X, as well as the associations among the two. In this work, a sparsity-promoting variant is proposed and a simple multiplicative algorithm is developed. The resulting sparse NMTF is further robustified to cope with presence of outliers in the data. A synthetic example illustrates the efficacy of the method. A novel application to cancer patient clustering and pathway analysis is presented using real datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIP.2012.6232906 | CIP |
Keywords | Field | DocType |
biology computing,cancer,data handling,genomics,matrix decomposition,cancer genomics,cancer patient clustering,column clusters,nonnegative matrix tri-factorization,pathway analysis,row clusters,simple multiplicative algorithm,sparse nmtf,sparse robust matrix tri-factorization,sparsity-promoting variant,robustness,bioinformatics,sparse matrices,gene expression,nonnegative matrix,optimization | Nonnegative matrix,Multiplicative function,Matrix (mathematics),Computer science,Matrix decomposition,Algorithm,Theoretical computer science,Robustness (computer science),Factorization,Cluster analysis,Sparse matrix | Conference |
ISBN | Citations | PageRank |
978-1-4673-1877-8 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-Jun Kim | 1 | 1003 | 62.52 |
| TaeHyun Hwang | 2 | 0 | 0.68 |
| G. B. Giannakis | 3 | 11464 | 1206.47 |