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Stat260/cs294: Randomized Algorithms for Matrices and Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mahoney, Michael |
| Copyright Year | 2015 |
| Abstract | Here, we will provide a spectral norm bound for the error of the approximation constructed by the BasicMatrixMultiplication algorithm. Recall that, given as input a m × n matrix A and an n× p matrix B, this algorithm randomly samples c columns of A and the corresponding rows of B to construct a m× c matrix C and a c× p matrix R such that CR ≈ AB, in the sense that some matrix norm ||AB −CR|| is small. The Frobenius norm bound we established before immediately implies a bound for the spectral norm, but in some cases we will need a better bound than can be obtained in this manner. Since, in this semester, we will only need a spectral norm bound for the spectial case that B = AT , that is all that we will consider here. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.stat.berkeley.edu/~mmahoney/f13-stat260-cs294/Lectures/lecture05.pdf |
| Alternate Webpage(s) | http://www.stat.berkeley.edu/~mmahoney/f13-stat260-cs294/Lectures/lecture04.pdf |
| Alternate Webpage(s) | http://cs.stanford.edu/people/mmahoney/f13-stat260-cs294/f13-project.pdf |
| Alternate Webpage(s) | http://www.stat.berkeley.edu/~mmahoney/f13-stat260-cs294/f13-project.pdf |
| Alternate Webpage(s) | http://www.stat.berkeley.edu/~mmahoney/f13-stat260-cs294/Lectures/lecture10.pdf |
| Alternate Webpage(s) | http://www.stat.berkeley.edu/~mmahoney/f13-stat260-cs294/Lectures/lecture09.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
