Name
Abstract | ||
|---|---|---|
A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We pre... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2472280 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Principal component analysis,Joints,Hyperspectral imaging,Compressed sensing,Image coding,Approximation algorithms | Approximation algorithm,Computer vision,Facial recognition system,Linear combination,Sparse PCA,Pattern recognition,Computer science,Sparse approximation,Hyperspectral imaging,Artificial intelligence,Compressed sensing,Principal component analysis | Journal |
Volume | Issue | ISSN |
24 | 12 | 1057-7149 |
Citations | PageRank | References |
11 | 0.52 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zohaib Khan | 1 | 45 | 5.56 |
| Faisal Shafait | 2 | 1324 | 88.97 |
| A. Mian | 3 | 1679 | 84.89 |