Name
Abstract | ||
|---|---|---|
In our “big data” age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are “data-oblivious” methods such as random projections and sketching, and “data-aware” methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIT.2021.3112821 | IEEE Transactions on Information Theory |
Keywords | DocType | Volume |
Principal component analysis,Data models,Transforms,Fans,Dimensionality reduction,Covariance matrices,Tools | Journal | 67 |
Issue | ISSN | Citations |
12 | 0018-9448 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Fan | 1 | 0 | 0.34 |
| SiFan Liu | 2 | 14 | 4.04 |
| Dobriban Edgar | 3 | 0 | 1.01 |
| David P. Woodruff | 4 | 2156 | 142.38 |