PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Random projections preserving the Hamming distance between words
Stefano Arca, Alberto Bertoni and Giuseppe Lipori
In: International Workshop on Neural Networks, 22-24 May 2008, Vietri sul Mare, Italy.


Random projections in the Euclidean space reduce the dimensionality of the data approximately preserving the distances between points. In the hypercube it holds a weaker property: random projections approximately preserve the distances within a certain range. In this note, we show an analogous result for the metric space <Sigma^d, d_H>, where Sigma^d is the set of words of length d on alphabet Sigma and d_H is the Hamming distance.

EPrint Type:Conference or Workshop Item (Oral)
Additional Information:ISBN:9781586039844
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1000
Deposited By:Giuseppe Lipori
Deposited On:08 March 2010