Nonlinear Random Projection is a dimensionality reduction technique that maps high-dimensional data into a lower-dimensional space using nonlinear transformations. Unlike linear projections, it captures complex structures and relationships in data. This approach supports machine learning tasks such as clustering, visualization, and anomaly detection. It is valued for efficiency in handling large-scale datasets while preserving geometric properties. The method bridges mathematical theory with practical data compression.
We introduce anchor vectors to monitor Rayleigh-backscattering variability in a fiber-optic computing system that performs nonlinear random projection for image classification. With a ~0.4-s calibration interval, system stability can be maintained with a linear decoder, achieving an average accuracy of 80%-90%.
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