The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks
Abstract
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 Sanger's 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%).
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Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Self-citationTraining and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
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What is principal component analysis?
Ringnér, M. (2008) · Nature Biotechnology · doi:10.1038/nbt0308-303 · Cited by 2,407
Simplified neuron model as a principal component analyzer
Oja, E. (1982) · Journal of Mathematical Biology · doi:10.1007/bf00275687 · Cited by 2,309
Lee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
Sub-nanosecond switching of a tantalum oxide memristor
Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/48/485203 · Cited by 701
Pattern classification by memristive crossbar circuits using ex situ and in situ training
Alibart, F., Zamanidoost, E., & Strukov, D. B. (2013) · Nature Communications · doi:10.1038/ncomms3072 · Cited by 642
Synaptic behaviors and modeling of a metal oxide memristive device
Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., & Lu, W. (2011) · Applied Physics A · doi:10.1007/s00339-011-6296-1 · Cited by 432
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Cited by
The future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138
Self-citationAnalogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292
Resistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04484-2 · Cited by 931
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
Metadata
- Authors
- Choi, S., Shin, J. H., Lee, J., Sheridan, P., & Lu, W. D.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-05-01
- Volume
- 17
- Issue
- 5
- Pages
- 3113-3118
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2609852068
- Cited by
- 189
- References
- 12
- FWCI
- 11.86
- Type
- journal article
- Language
- en
- Open access
- closed
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