NOFE is a neural operator method for continuous dimensionality reduction using Graph Kernel Operators that outperforms PCA, t-SNE and UMAP on local structure preservation and sampling independence in datasets including ERA5 climate reanalysis.
Overview and comparative study of dimensionality reduction techniques for high dimensional data.Information Fusion, 59:44–58, July 2020
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The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.
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NOFE - Neural Operator Function Embedding
NOFE is a neural operator method for continuous dimensionality reduction using Graph Kernel Operators that outperforms PCA, t-SNE and UMAP on local structure preservation and sampling independence in datasets including ERA5 climate reanalysis.
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Assessing the impact of dimensionality reduction on clustering performance -- a systematic study
The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.