Kurtosis is the classical method of measuring nongaussianity. When data is preprocessed to have unit variance, kurtosis is equal to the fourth moment of the data. In an intuitive sense, kurtosis measured how "spikiness" of a distribution or the size of the tails. Kurtosis is extremely simple to calculate, however, it is very sensitive to outliers in the data set. It values may be based on only a few values in the tails which means that its statistical significance is poor. Kurtosis is not robust enough for ICA.
The entropy of a discrete signal is equal to the sum of the products of probability of each event and the log of those probabilities. A value called differential entropy can be found for continuous function that uses the integral of the function times the log of the function. Negentropy is simply the differential entropy of a signal y, minus the differential entropy of a gaussian signal with the same covariance of y. Negentropy is always positive and is zero only if the signal is a pure gaussian signal. It is stable but difficult to calculate.
An approach is used to approximate negentropy in a way that is computationally less expensive than calculating negentropy directly, but still more stable than kurtosis. The following equation approximates negentropy.
In this equation G(x) is some nonquadratic function, v is a gaussian variable with unit variance and zero mean, and ki is some constant value. If G(x) = x4 this equation becomes equal to kurtosis. There are functions that can be used for G that give a good approximation to negentropy and are less sensitive to outliers than kurtosis. Two commonly used functions are:
These are called contrast functions.