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transform multiple one-hot encoded

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Is SVD a good way to transform multiple one-hot encoded attributes into a vector representation? • /r/MachineLearning

@machinelearnbotOct-4-2016, 14:07:17 GMT

I would guess not, unless you have many small one-hot layers that are correlated with each other. What does work is if you can get something similar to'word embeddings' e.g. from training a compressive autoencoder on as much unlabeled data as you can; either from each of the attributes separately or as a merged representation, depending on what data you have.

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