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.
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Kryzhanovsky, V., Malsagov, M., Tomas, J.A.C., Zhelavskaya, I. (2014). High-Dimensional Binary Pattern Classification by Scalar Neural Network Tree. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_22
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DOI: https://doi.org/10.1007/978-3-319-11179-7_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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