24,633 research outputs found
Linear Hashing is Awesome
We consider the hash function where
are chosen uniformly at random from . We prove that when we
use in hashing with chaining to insert elements into a table of size
the expected length of the longest chain is
. The proof also generalises to give the same
bound when we use the multiply-shift hash function by Dietzfelbinger et al.
[Journal of Algorithms 1997].Comment: A preliminary version appeared at FOCS'1
Wear Minimization for Cuckoo Hashing: How Not to Throw a Lot of Eggs into One Basket
We study wear-leveling techniques for cuckoo hashing, showing that it is
possible to achieve a memory wear bound of after the
insertion of items into a table of size for a suitable constant
using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically,
showing that it significantly improves on the memory wear performance for
classic cuckoo hashing and linear probing in practice.Comment: 13 pages, 1 table, 7 figures; to appear at the 13th Symposium on
Experimental Algorithms (SEA 2014
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Supervised hashing aims to map the original features to compact binary codes
that are able to preserve label based similarity in the Hamming space.
Non-linear hash functions have demonstrated the advantage over linear ones due
to their powerful generalization capability. In the literature, kernel
functions are typically used to achieve non-linearity in hashing, which achieve
encouraging retrieval performance at the price of slow evaluation and training
time. Here we propose to use boosted decision trees for achieving non-linearity
in hashing, which are fast to train and evaluate, hence more suitable for
hashing with high dimensional data. In our approach, we first propose
sub-modular formulations for the hashing binary code inference problem and an
efficient GraphCut based block search method for solving large-scale inference.
Then we learn hash functions by training boosted decision trees to fit the
binary codes. Experiments demonstrate that our proposed method significantly
outperforms most state-of-the-art methods in retrieval precision and training
time. Especially for high-dimensional data, our method is orders of magnitude
faster than many methods in terms of training time.Comment: Appearing in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2014, Ohio, US
Linear Hashing
Consider the set H of all linear (or affine) transformations between two vector spaces over a finite field F. We study how good H is as a class of hash functions, namely we consider hashing a set S of sizen into a range having the same cardinality n by a randomly chosen function from H and look at the expected size of the largest hash bucket. H is a universal class of hash functions for any finite field, butwith respect to our measure different fields behave differently
Hashing protocol for distilling multipartite CSS states
We present a hashing protocol for distilling multipartite CSS states by means
of local Clifford operations, Pauli measurements and classical communication.
It is shown that this hashing protocol outperforms previous versions by
exploiting information theory to a full extent an not only applying CNOTs as
local Clifford operations. Using the information-theoretical notion of a
strongly typical set, we calculate the asymptotic yield of the protocol as the
solution of a linear programming problem.Comment: 13 pages, 3 figures, RevTeX
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
Recently it was shown that the problem of Maximum Inner Product Search (MIPS)
is efficient and it admits provably sub-linear hashing algorithms. Asymmetric
transformations before hashing were the key in solving MIPS which was otherwise
hard. In the prior work, the authors use asymmetric transformations which
convert the problem of approximate MIPS into the problem of approximate near
neighbor search which can be efficiently solved using hashing. In this work, we
provide a different transformation which converts the problem of approximate
MIPS into the problem of approximate cosine similarity search which can be
efficiently solved using signed random projections. Theoretical analysis show
that the new scheme is significantly better than the original scheme for MIPS.
Experimental evaluations strongly support the theoretical findings.Comment: arXiv admin note: text overlap with arXiv:1405.586
- …