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Found 3 papers, 1 papers with code
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods
The implementation of strategies for fault detection and diagnosis on rotating electrical machines is crucial for the reliability and safety of modern industrial systems.
Functional Diffusion Maps
Nowadays many real-world datasets can be considered as functional, in the sense that the processes which generate them are continuous.
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis
Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have attached a target that corresponds to the value of an unknown function.
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