Online representation learning with single and multi-layer Hebbian networks for image classification
conference contributionposted on 2017-06-20, 12:31 authored by Yanis Bahroun, Andrea SoltoggioAndrea Soltoggio
Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks.
- Computer Science
Published inInternational Conference on Artificial Neural Networks
CitationBAHROUN, Y. and SOLTOGGIO, A., 2017. Online representation learning with single and multi-layer Hebbian networks for image classification. IN: Lintas, A. ...et al. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2017 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings. New York: Springer, Part 1, pp.354-363.
- AM (Accepted Manuscript)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper was presented at the 26th International Conference on Artificial Neural Networks, Alghero, Sardinia, 11-15th September. This is a pre-copyedited version of a contribution published in Lintas, A. ...et al. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2017 published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-68612-7.
Book seriesLecture Notes in Computer Science; 10614