RIPTIDE: Segmenting data using multiple resolutions

  • Armstrong T
  • Oates T
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Abstract

Segmenting real-valued data, be it speech waveforms into words and phrases or temperature readings into environmental epochs, is a challenging, open problem. We introduce an unsupervised, domain-independent algorithm, RIP-TIDE, that discovers segments in real-valued time series data while constructing a hierarchy of segments. Our top-down approach begins with a coarse approximation of the input data, finds segment boundaries, and recursively considers discovered segments with a finer resolution. We demonstrate the drawbacks of an existing segmentation algorithm and the multiresolution capabilities of a discretization method for time series. © 2007 IEEE.

Author-supplied keywords

  • Perceptual Organization
  • Segmentation
  • Unsupervised Learning
  • Word Discovery

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