Name
Abstract | ||
|---|---|---|
We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.spl.2011.02.001 | Statistics & Probability Letters |
Keywords | Field | DocType |
Matrix factorization,Nonnegative matrix factorization,High-dimensional data,Microarray gene-expression data,Supervised classification | Clustering high-dimensional data,Data set,Dimensionality reduction,Matrix decomposition,Algorithm,FSA-Red Algorithm,Differentiable function,Non-negative matrix factorization,Statistics,Probability theory,Mathematics | Journal |
Volume | Issue | ISSN |
81 | 7 | 0167-7152 |
Citations | PageRank | References |
10 | 0.69 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Nikulin | 1 | 99 | 17.28 |
| Tian-Hsiang Huang | 2 | 50 | 6.12 |
| Shu Kay Ng | 3 | 161 | 13.17 |
| Suren I. Rathnayake | 4 | 10 | 0.69 |
| McLachlan Geoffrey J. | 5 | 1787 | 126.70 |