Hashing embeddings of optimal dimension, with applications to linear least squares
The aim of this paper is two-fold: firstly, to present subspace embedding properties for s-hashing sketching matrices, with s ≥ 1, that are optimal in the projection dimension m of the sketch, namely, m = O(d), where d is the dimension of the subspace. A diverse set of results are presented that add...
| Main Authors: | , , |
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| Format: | Internet publication |
| Language: | English |
| Published: |
2021
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