Shift-Invariant Sparse Coding for Audio Classification

@inproceedings{Grosse2007ShiftInvariantSC,
  title={Shift-Invariant Sparse Coding for Audio Classification},
  author={Roger Grosse},
  year={2007}
}
Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to be useful for self-taught learning, in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from different classes than that… CONTINUE READING

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