Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with. Here is an example of code implementing two common dimensionality reduction algorithms, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), in C++.
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Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with.
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guillaumelauzier/Dimensionality_reduction_algorithms
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Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with.
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