Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks

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Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning. Using Sangers rule, that is, the generalized Hebbian algorithm, the principal components were obtained as the memristor conductances in the network after training. The network was then used to analyze sensory data from a standard breast cancer screening database with high classification success rate (97.1%).
Publisher
AMER CHEMICAL SOC
Issue Date
2017-05
Language
English
Article Type
Article
Citation

NANO LETTERS, v.17, no.5, pp.3113 - 3118

ISSN
1530-6984
DOI
10.1021/acs.nanolett.7b00552
URI
http://hdl.handle.net/10203/247674
Appears in Collection
EE-Journal Papers(저널논문)
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