Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
SIAM Journal on Computing
Dimension Reduction in the \ell _1 Norm
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Algorithmic Applications of Low-Distortion Geometric Embeddings
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
On the impossibility of dimension reduction in l1
Journal of the ACM (JACM)
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Stable distributions, pseudorandom generators, embeddings, and data stream computation
Journal of the ACM (JACM)
Metric structures in L1: dimension, snowflakes, and average distortion
European Journal of Combinatorics
Weak ε-nets and interval chains
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Hi-index | 0.00 |
Let X be a normed space that satisfies the Johnson-Lindenstrauss lemma (J--L lemma, in short) in the sense that for any integer n and any x1,...,xn ε X there exists a linear mapping L: X → F, where F ⊆ X is a linear subspace of dimension O(log n), such that ||xi - xj|| ≤ ||L(xi) - L(xj)|| ≤ O(1) · ||xi - xj|| for all i, j ε {1,..., n). We show that this implies that X is almost Euclidean in the following sense: Every n-dimensional subspace of X embeds into Hilbert space with distortion [EQUATION]. On the other hand, we show that there exists a normed space Y which satisfies the J-L lemma, but for every n there exists an n-dimensional subspace En ⊆ Y whose Euclidean distortion is at least 2Ω(α(n)), where α is the inverse Ackermann function.