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On Shift-Invariant Sparse Coding

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 3195)

Abstract

The goals of this paper are: 1) the introduction of a shift-invariant sparse coding model together with learning rules for this model; 2) the comparison of this model to the traditional sparse coding model; and 3) the analysis of some limitations of the newly proposed approach. To evaluate the model we will show that it can learn features from a toy problem as well as note-like features from a polyphonic piano recording. We further show that the shift-invariant model can help in overcoming some of the limitations of the traditional model which occur when learning less functions than are present in the true generative model. We finally show a limitation of the proposed model for problems in which mixtures of continuously shifted functions are used.

Keywords

  • Sparse Code
  • Blind Source Separation
  • Neural Information Processing System
  • Inference Process
  • Sparse Component

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-30110-3_152
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© 2004 Springer-Verlag Berlin Heidelberg

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Blumensath, T., Davies, M. (2004). On Shift-Invariant Sparse Coding. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_152

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_152

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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