Difference between revisions of "Open Problems:54"
(Created page with "{{Header |title=Faster JL Dimensionality Reduction |source=dortmund12 |who=Jelani Nelson }} The standard Johnson-Lindenstrauss lemma states the following: for any $0<\epsilon<...") |
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The main question is to construct $A$'s that admit faster computation time of $Ax$. There are several directions to try to obtain more efficient $A$: | The main question is to construct $A$'s that admit faster computation time of $Ax$. There are several directions to try to obtain more efficient $A$: | ||
| − | * Fast JL (FFT-based) | + | * Fast JL (FFT-based): Here, the runtime is of the form $O(d\log d + \hbox{poly}(k))$ to compute $Ax$ ($d\log d$ is usually the most significant term). |
| − | * Sparse JL | + | * Sparse JL: Here, the runtime is of the form $O(\epsilon k\|x\|_0+k)$, where $\|x\|_0$ is the number of non-zero coordinates of $x$ (i.e., it works well for sparse vectors). |
| − | '''Question''' | + | '''Question:''' Can one obtain a JL matrix $A$ such that one can compute $Ax$ in time $\tilde O(\|x\|_0+k)$ ? |
| − | One possible avenue would be by considering a | + | One possible avenue would be by considering a “random” $k$ by $k$ submatrix of the FFT matrix. This may or may not lead to the desired result. |
Revision as of 04:46, 12 December 2012
| Suggested by | Jelani Nelson |
|---|---|
| Source | Dortmund 2012 |
| Short link | https://sublinear.info/54 |
The standard Johnson-Lindenstrauss lemma states the following: for any $0<\epsilon<1/2$, any $x_1\ldots x_n\in \R^d$, there exists $A\in \R^{k\times d}$ with $k=O(1/\epsilon^2\cdot \log n)$, such that for any $i,j$ we have $\|Ax_i-Ax_j\|_2=(1\pm \epsilon)\|x_i-x_j\|_2$.
The main question is to construct $A$'s that admit faster computation time of $Ax$. There are several directions to try to obtain more efficient $A$:
- Fast JL (FFT-based): Here, the runtime is of the form $O(d\log d + \hbox{poly}(k))$ to compute $Ax$ ($d\log d$ is usually the most significant term).
- Sparse JL: Here, the runtime is of the form $O(\epsilon k\|x\|_0+k)$, where $\|x\|_0$ is the number of non-zero coordinates of $x$ (i.e., it works well for sparse vectors).
Question: Can one obtain a JL matrix $A$ such that one can compute $Ax$ in time $\tilde O(\|x\|_0+k)$ ?
One possible avenue would be by considering a “random” $k$ by $k$ submatrix of the FFT matrix. This may or may not lead to the desired result.
