Autoencoders and Sparsity

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So far, we have described the application of neural networks to supervised learning, in which we have labeled
So far, we have described the application of neural networks to supervised learning, in which we have labeled
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training examples.  Now suppose we have only unlabeled training examples set <math>\textstyle \{x^{(1)}, x^{(2)}, x^{(3)}, \ldots\}</math>,
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training examples.  Now suppose we have only a set of unlabeled training examples <math>\textstyle \{x^{(1)}, x^{(2)}, x^{(3)}, \ldots\}</math>,
where <math>\textstyle x^{(i)} \in \Re^{n}</math>.  An
where <math>\textstyle x^{(i)} \in \Re^{n}</math>.  An
'''autoencoder''' neural network is an unsupervised learning algorithm that applies backpropagation,
'''autoencoder''' neural network is an unsupervised learning algorithm that applies backpropagation,
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{{Sparse_Autoencoder}}
{{Sparse_Autoencoder}}
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{{Languages|自编码算法与稀疏性|中文}}

Latest revision as of 12:43, 7 April 2013

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