Reduced-Feature Hybrid Machine Learning for Coal Ash Sintering-Temperature Prediction Using Chemistry and Physicochemical Descriptors. [PDF]
ACS OmegaAl-Otoom A.
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ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms. [PDF]
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Identification of Acoustic Emission Spectrograms from Limestone Fracturing Based on a Novel Deep Learning Model. [PDF]
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An Interpretable Evolutionary-Fuzzy Framework for EEG Feature Extraction: Application to Chemosensory Task Classification. [PDF]
Sensors (Basel)Seweryńska Z, Aydemir Ö.
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Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models. [PDF]
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Visuomotor coordination on the road: Low-dimensional representations reveal adaptive, context-dependent reductions in the dimensionality of natural driving behavior. [PDF]
PLoS Comput BiolMadrid-Carvajal J +2 more
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Nonlinear dimensionality reduction and visualization
, 2009 Verleysen, Michel, Lee, John
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Nonlinear Dimensionality Reduction by Locally Linear Embedding
Science, 2000 Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data.
Lawrence K. Saul
exaly +5 more sources
Supervised Nonlinear Dimensionality Reduction for Visualization and Classification [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, 2005 —When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques.
Zhi-Hua Zhou
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