Efficient Implementation of Self-Organizing Map for Sparse Input Data

Josué Melka, Jean-Jacques Mariage

Abstract

Neural-based learning algorithms, which in most cases implement a lengthy iterative convergence procedure, are often hardly adapted to very sparse input data, both due to practical issues concerning time and memory usage, and to the inherent difficulty of learning in high dimensional space. However, the description of many real-world data sets is sparse by nature, and learning algorithms must circumvent this barrier. This paper proposes adaptations of the standard and the batch versions of the Self-Organizing Map algorithm, specifically fine-tuned for high dimensional sparse data, with parallel implementation efficiency in mind. We extensively evaluate the performance of both adaptations on a set of experiments carried out on several real and artificial large benchmark datasets of sparse format from the LIBSVM Data: Classi­fication. Results show that our approach brings a significant improvement in execution time.

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Paper Citation


in Harvard Style

Melka J. and Mariage J. (2017). Efficient Implementation of Self-Organizing Map for Sparse Input Data.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 54-63. DOI: 10.5220/0006499500540063


in Bibtex Style

@conference{ijcci17,
author={Josué Melka and Jean-Jacques Mariage},
title={Efficient Implementation of Self-Organizing Map for Sparse Input Data},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={54-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006499500540063},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Efficient Implementation of Self-Organizing Map for Sparse Input Data
SN - 978-989-758-274-5
AU - Melka J.
AU - Mariage J.
PY - 2017
SP - 54
EP - 63
DO - 10.5220/0006499500540063