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Abstract Details
Activity Number:
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63
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #302975 |
Title:
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A Frequency Domain EM Algorithm to Detect Similar Dynamics in Time Series with Applications to Spike Sorting and Macro-Economics
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Author(s):
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Georg M. Goerg*+
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Companies:
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Carnegie Mellon University
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Address:
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5000 Forbes Avenue, Pittsburgh, PA, 15213,
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Keywords:
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statistical learning
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Abstract:
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In this work I propose a frequency domain adaptation of the Expectation Maximization (EM) algorithm to separate a family of sequential observations in classes of similar dynamic structure, which can either mean non-stationary signals of similar shape, or stationary signals with similar auto-covariance function. It does this by viewing the magnitude of the discrete Fourier transform (DFT) of the signals (or power spectrum) as a probability density/mass function (pdf/pmf) on the unit circle: signals with similar dynamics have similar pdfs; distinct patterns have distinct pdfs. An advantage of this approach is that it does not rely on any parametric form of the dynamic structure, but can be used for non-parametric, robust and model-free classification. Applications to neural spike sorting (non-stationary) and pattern-recognition in socio-economic time series (stationary) demonstrate the usefulness and wide applicability of the proposed method.
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