Abstract
Similarity is a key concept to quantify temporal signals or static measurements. Similarity is difficult to define mathematically, however, one never really thinks too much about this difficulty and naturally translates similarity by correlation. This is one more example of how engrained second-order moment descriptors of the probability density function really are in scientific thinking. Successful engineering or pattern recognition solutions from these methodologies rely heavily on the Gaussianity and linearity assumptions, exactly for the same reasons discussed in Chapter 3.
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Liu, W., Pokharel, P., Xu, J., Seth, S. (2010). Correntropy for Random Variables: Properties and Applications in Statistical Inference. In: Information Theoretic Learning. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1570-2_10
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