Name
Abstract | ||
|---|---|---|
Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods. © Springer International Publishing Switzerland 2016. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-42294-7_25 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
Non-negative matrix factorization, Clustering, Genomic data, Dimensionality reduction | Coefficient matrix,Dimensionality reduction,Pattern recognition,Computer science,Matrix decomposition,Artificial intelligence,Non-negative matrix factorization,Data dimensionality reduction,Biclustering,Cluster analysis | Conference |
Volume | ISSN | Citations |
9772 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hou Mi-Xiao | 1 | 1 | 2.73 |
| Gao Ying-Lian | 2 | 29 | 18.73 |
| Liu Jin-Xing | 3 | 40 | 16.11 |
| Junliang Shang | 4 | 42 | 14.78 |
| Chun-hou Zheng | 5 | 732 | 71.79 |