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TR10-162 | 30th October 2010 19:48
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#### Deterministic Construction of a high dimensional $\ell_p$ section in $\ell_1^n$ for any $p<2$

**Abstract:**
For any $00$, we give an efficient

deterministic construction of a linear subspace $V \subseteq

\R^n$, of dimension $(1-\epsilon)n$ in which the $\ell_p$ and

$\ell_r$ norms are the same up to a multiplicative factor of

$\poly(\epsilon^{-1})$ (after the correct normalization). As a

corollary we get a deterministic compressed sensing algorithm

(Base Pursuit) for a new range of parameters. In particular, for

any constant $\epsilon>0$ and $p<2$, we obtain a linear operator

$A:\R^n \rightarrow \R^{\epsilon n}$ with the $\ell_1/\ell_p$

guarantee for $(n \cdot \poly(\epsilon))$-sparse vectors. Namely,

let $x$ be a vector in $\R^n$ whose $\ell_1$ distance from a

$k$-sparse vector (for some $k=n \cdot \poly(\epsilon)$) is

$\delta$. The algorithm, given $Ax$ as input, outputs an $n$

dimensional vector $y$ such that $||x-y||_p \leq \delta

k^{1/p-1}$. In particular this gives a weak form of the

$\ell_2/\ell_1$ guarantee.

Our construction has the additional benefit that when viewed as a

matrix, $A$ has at most $O(1)$ non-zero entries in each row. As a

result, both the encoding (computing $Ax$) and decoding

(retrieving $x$ from $Ax$) can be computed efficiently.