Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is primarily a linear dimensionality reduction technique that factorizes a nonnegative data matrix into two smaller nonnegative matrices: one that represents the basis of the new subspace and the second that holds the coefficients of all the data points in that new space. In principle, the nonnegativity constraint forces the representation to be sparse and pa... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1162/neco_a_00980 | Neural Computation |
Field | DocType | Volume |
Data point,Mathematical optimization,Dimensionality reduction,Subspace topology,Pattern recognition,Matrix (mathematics),Minimum description length,Synthetic data,Non-negative matrix factorization,Artificial intelligence,Mathematics,Machine learning | Journal | 29 |
Issue | ISSN | Citations |
8 | 0899-7667 | 4 |
PageRank | References | Authors |
0.40 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Squires | 1 | 4 | 1.08 |
| Adam Prügel-Bennett | 2 | 472 | 37.33 |
| Mahesan Niranjan | 3 | 775 | 120.43 |