Name
Abstract | ||
|---|---|---|
Different kinds of features describe different aspects of image data, and each feature can be treated as a view when we take it as a particular understanding of images. Leveraging multiple views provides a richer and comprehensive description than using only a single view. However, multiview data are often represented by high-dimensional heterogeneous features, so it is meaningful to find a low-di... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSVT.2016.2607842 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Manifolds,Robustness,Kernel,Optimization,Sparse matrices,Feature extraction,Visualization | Data set,Dimensionality reduction,Computer science,Robustness (computer science),Probabilistic latent semantic analysis,Artificial intelligence,Discriminative model,Kernel (linear algebra),Computer vision,Pattern recognition,Visualization,Feature extraction,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 2 | 1051-8215 |
Citations | PageRank | References |
4 | 0.38 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Xue | 1 | 72 | 14.60 |
| Guorong Li | 2 | 196 | 19.93 |
| Shuhui Wang | 3 | 596 | 51.45 |
| Weigang Zhang | 4 | 57 | 7.85 |
| Qingming Huang | 5 | 3919 | 267.71 |