High-Dimensional Binary Pattern Classification by Scalar Neural Network Tree

Vladimir Kryzhanovsky, Magomed Malsagov, Juan Antonio Clares Tomas, Irina Zhelavskaya

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, BBF-tree, spill-tree) stop working as the dimensionality of the space grows (N > 1000). With such high dimensionality, our algorithm works 7 times faster than the exhaustive search algorithm.