Editing Information bottleneck method (section)

===Neural network/fuzzy logic analogies===
This algorithm is somewhat analogous to a neural network with a single hidden layer. The internal nodes are represented by the clusters <math>c_j \,</math> and the first and second layers of network weights are the conditional probabilities <math>p(c_j | x_i) \,</math> and <math>p(y_k | c_j) \,</math> respectively. However, unlike a standard neural network, the algorithm relies entirely on probabilities as inputs rather than the sample values themselves, while internal and output values are all conditional probability density distributions. Nonlinear functions are encapsulated in distance metric <math>f(.) \,</math> (or ''influence functions/radial basis functions'') and transition probabilities instead of [[sigmoid function]]s.

The Blahut-Arimoto three-line algorithm converges rapidly, often in tens of iterations, and by varying <math>\beta \,</math>, <math>\lambda \,</math> and <math>f \,</math> and the cardinality of the clusters, various levels of focus on features can be achieved.

The statistical soft clustering definition <math>p(c_i | x_j) \,</math> has some overlap with the verbal fuzzy membership concept of [[fuzzy logic]].