Deep Bottleneck Classifiers in Supervised Dimension Reduction

@inproceedings{Parviainen2010DeepBC,
  title={Deep Bottleneck Classifiers in Supervised Dimension Reduction},
  author={Elina Parviainen},
  booktitle={ICANN},
  year={2010}
}
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, which gives a low dimensional representation for the data when the full network is trained to minimize reconstruction error. We propose using a deep bottlenecked neural network in supervised dimension reduction. Instead of trying to reproduce the data, the network is trained to perform classification. Pretraining with… CONTINUE READING
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