Regularized linear autoencoders recover the principal components, eventually

13 Jul 2020Xuchan BaoJames LucasSushant SachdevaRoger Grosse

Our understanding of learning input-output relationships with neural nets has improved rapidly in recent years, but little is known about the convergence of the underlying representations, even in the simple case of linear autoencoders (LAEs). We show that when trained with proper regularization, LAEs can directly learn the optimal representation -- ordered, axis-aligned principal components... (read more)

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