Skip to main content

Dynamic spectrum matching with one-shot learning

Liu, Jinchao, Gibson, Stuart J., Mills, James, Osadchy, Margarita (2019) Dynamic spectrum matching with one-shot learning. Chemometrics and Intelligent Laboratory Systems, 184 . pp. 175-181. ISSN 0169-7439. (doi:10.1016/j.chemolab.2018.12.005) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only until 18 December 2019.

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Contact us about this Publication Download (396kB)
[img]
Official URL
https://doi.org/10.1016/j.chemolab.2018.12.005

Abstract

Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identifying substances from noisy spectra without the need for additional preprocessing. However, their application in practical spectroscopy is restricted due to two reasons. First the effectiveness of classification using CNNs diminishes rapidly when only a small number of spectra per substance are available for training (which is a typical situation in real applications). Secondly, to accommodate new, previously unseen, substance classes the network must be retrained which is computationally intensive. Here we address these issues by reformulating a multi-class classification problem with a large number of classes to a binary classification problem for which the available data is sufficient for representation learning. Hence, we define the learning task as identifying pairs of inputs as belonging to the same class or different classes. We achieve this using a Siamese convolutional neural network. A novel sampling strategy is proposed to address the imbalance problem in training the Siamese network. The trained network can classify samples of previously unseen substance classes using just a single reference sample (termed as one-shot learning in the machine learning community). Our results on three independent Raman datasets demonstrate much better accuracy than other practical systems to date, while allowing effortless updates of the system's database with new substance classes.

Item Type: Article
DOI/Identification number: 10.1016/j.chemolab.2018.12.005
Uncontrolled keywords: Spectrum matching, Siamese network, One-shot learning, Convolutional neural networks
Subjects: Q Science
Divisions: Faculties > Sciences > School of Physical Sciences
Depositing User: Stuart Gibson
Date Deposited: 08 Jan 2019 15:23 UTC
Last Modified: 30 May 2019 08:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71569 (The current URI for this page, for reference purposes)
Gibson, Stuart J.: https://orcid.org/0000-0002-7981-241X
  • Depositors only (login required):

Downloads

Downloads per month over past year