Higher-Order Statistical Properties Arising from the Non-Stationarity of Natural Signals

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Lucas Parra, Clay Spence, Paul Sajda


We present evidence that several higher-order statistical proper(cid:173) ties of natural images and signals can be explained by a stochastic model which simply varies scale of an otherwise stationary Gaus(cid:173) sian process. We discuss two interesting consequences. The first is that a variety of natural signals can be related through a com(cid:173) mon model of spherically invariant random processes, which have the attractive property that the joint densities can be constructed from the one dimensional marginal. The second is that in some cas(cid:173) es the non-stationarity assumption and only second order methods can be explicitly exploited to find a linear basis that is equivalent to independent components obtained with higher-order methods. This is demonstrated on spectro-temporal components of speech.