Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks

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dc.contributor.authorChoi, Shinhyunko
dc.contributor.authorShin, Jong Hoonko
dc.contributor.authorLee, Jihangko
dc.contributor.authorSheridan, Patrickko
dc.contributor.authorLu, Wei D.ko
dc.date.accessioned2018-12-20T05:13:16Z-
dc.date.available2018-12-20T05:13:16Z-
dc.date.created2018-12-03-
dc.date.created2018-12-03-
dc.date.issued2017-05-
dc.identifier.citationNANO LETTERS, v.17, no.5, pp.3113 - 3118-
dc.identifier.issn1530-6984-
dc.identifier.urihttp://hdl.handle.net/10203/247674-
dc.description.abstractMemristors 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%).-
dc.languageEnglish-
dc.publisherAMER CHEMICAL SOC-
dc.titleExperimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks-
dc.typeArticle-
dc.identifier.wosid000401307300054-
dc.identifier.scopusid2-s2.0-85019251877-
dc.type.rimsART-
dc.citation.volume17-
dc.citation.issue5-
dc.citation.beginningpage3113-
dc.citation.endingpage3118-
dc.citation.publicationnameNANO LETTERS-
dc.identifier.doi10.1021/acs.nanolett.7b00552-
dc.contributor.localauthorChoi, Shinhyun-
dc.contributor.nonIdAuthorShin, Jong Hoon-
dc.contributor.nonIdAuthorLee, Jihang-
dc.contributor.nonIdAuthorSheridan, Patrick-
dc.contributor.nonIdAuthorLu, Wei D.-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorUnsupervised learning-
dc.subject.keywordAuthorprincipal component analysis-
dc.subject.keywordAuthorclustering-
dc.subject.keywordAuthorneuromorphic computing-
dc.subject.keywordAuthorartificial neural network-
dc.subject.keywordAuthorRRAM-
dc.subject.keywordPlusPRINCIPAL COMPONENT ANALYSIS-
dc.subject.keywordPlusNEUROMORPHIC SYSTEMS-
dc.subject.keywordPlusOXIDE MEMRISTORS-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusCIRCUITS-
dc.subject.keywordPlusDEVICE-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusMODEL-
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