Name
Title | ||
|---|---|---|
Simultaneous Dimensionality Reduction and Classification via Dual Embedding Regularized Nonnegative Matrix Factorization. |
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is a wellknown paradigm for data representation. Traditional NMF-based classification methods first perform NMF or one of its variants on input data samples to obtain their low-dimensional representations, which are successively classified by means of a typical classifier [e.g., k-nearest neighbors (KNN) and support vector machine (SVM)]. Such a stepwise mann... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2019.2907054 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Computational complexity,Dimensionality reduction,Writing,Indexes,Optimization,Benchmark testing | Dimensionality reduction,Embedding,Pattern recognition,Matrix (mathematics),Support vector machine,Artificial intelligence,Non-negative matrix factorization,Classifier (linguistics),Mathematics,Computational complexity theory,Constrained optimization | Journal |
Volume | Issue | ISSN |
28 | 8 | 1057-7149 |
Citations | PageRank | References |
6 | 0.41 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhui Wu | 1 | 44 | 4.65 |
| Sam Kwong | 2 | 4590 | 315.78 |
| Junhui Hou | 3 | 395 | 49.84 |
| Yuheng Jia | 4 | 93 | 13.13 |
| Horace H. S. Ip | 5 | 1521 | 150.88 |