Name
Title | ||
|---|---|---|
Low rank approximation and decomposition of large matrices using error correcting codes |
Abstract | ||
|---|---|---|
Low rank approximation is an important tool used in many applications of signal processing and machine learning. Recently, randomized sketching algorithms were proposed to effectively construct low rank approximations and obtain approximate singular value decompositions of large matrices. Similar ideas were used to solve least squares regression problems. In this paper, we show how matrices from e... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIT.2017.2723898 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
Sparse matrices,Matrix decomposition,Error correction codes,Approximation algorithms,Transforms,Error correction,Algorithm design and analysis | Discrete mathematics,Approximation algorithm,Combinatorics,Singular value,Matrix (mathematics),Matrix norm,Low-rank approximation,Linear least squares,Approximation error,Mathematics,Random matrix | Journal |
Volume | Issue | ISSN |
63 | 9 | 0018-9448 |
Citations | PageRank | References |
3 | 0.41 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| shashanka ubaru | 1 | 58 | 8.97 |
| Arya Mazumdar | 2 | 307 | 41.81 |
| Yousef Saad | 3 | 1940 | 254.74 |