Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks
| Autor: | Jong Hoon Shin, Shinhyun Choi, Patrick Sheridan, Wei Lu, Jihang Lee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Feature extraction Bioengineering 02 engineering and technology Memristor 01 natural sciences law.invention law Generalized Hebbian Algorithm 0103 physical sciences General Materials Science 010302 applied physics Artificial neural network business.industry Mechanical Engineering Dimensionality reduction Pattern recognition General Chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics Resistive random-access memory Neuromorphic engineering Unsupervised learning Artificial intelligence 0210 nano-technology business |
| Zdroj: | Nano Letters. 17:3113-3118 |
| ISSN: | 1530-6992 1530-6984 |
| Popis: | 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%). |
| Databáze: | OpenAIRE |
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