Distorted high-dimensional binary patterns search by scalar neural network tree

Abstract

The paper offers an algorithm (SNN-tree) that extends the binary tree search algorithm so that it can deal with distorted input vectors. Perceptrons are the tree nodes. The algorithm features an iterative solution search and stopping criterion. Unlike the SNN-tree algorithm, popular methods (LSH, k-d tree, BBFtree, spill-tree) stop working as the dimensionality of the space grows (N > 1000). In this paper we managed to obtain an estimate of the upper bound on the error probability for SNN-tree algorithm. The proposed algorithm works much faster than exhaustive search (26 times faster at N = 10000).