Learning sparse filter bank transforms with convolutional ICA

Johannes Ballé, Eero P. Simoncelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Independent Component Analysis (ICA) is a generalization of Principal Component Analysis that optimizes a linear transformation to whiten and sparsify a family of source signals. The computational costs of ICA grow rapidly with dimensionality, and application to high-dimensional data is generally achieved by restricting to small windows, violating the translation-invariant nature of many real-world signals, and producing blocking artifacts in applications. Here, we reformulate the ICA problem for transformations computed through convolution with a bank of filters, and develop a generalization of the fastICA algorithm for optimizing the filters over a set of example signals. This results in a substantial reduction of computational complexity and memory requirements. When applied to a database of photographic images, the method yields bandpass oriented filters, whose responses are sparser than those of orthogonal wavelets or block DCT, and slightly more heavy-tailed than those of block ICA, despite fewer model parameters.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479957514
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014


  • convolutional filters
  • fastICA
  • filter bank
  • independent component analysis
  • sparsity
  • stationarity

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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