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GitHub - kevinzakka/nca: A PyTorch implementation of Neighbourhood Components An...
source link: https://github.com/kevinzakka/nca
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README.md
Neighbourhood Components Analysis
A PyTorch implementation of Neighbourhood Components Analysis by J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov.
NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.
API
# instantiate nca object and initialize with # an identity matrix nca = NCA(dim=2, init="identity") # fit an nca model to a dataset # normalize the input data before # running the optimization nca.train(X, y, batch_size=64, normalize=True) # apply the learned linear map to the data X_nca = nca(X)
Dimensionality Reduction
We generate a 3-D dataset where the first 2 dimensions are concentric rings and the third dimension is Gaussian noise. We plot the result of PCA and NCA with 2 components.
Notice how PCA has failed to project out the noise, a result of a high noise variance in the third dimension. You can try lowering it (e.g. 0.1) using the --sigma command line argument to see its effect on PCA.
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