Title :
Entropy manipulation of arbitrary nonlinear mappings
Author :
Fisher, John W., III ; Principe, José C.
Author_Institution :
Lab. of Comput. Neuroeng., Florida Univ., Gainesville, FL, USA
Abstract :
We discuss an unsupervised learning method which is driven by an information theoretic based criterion. The method differs from previous work in that it is extensible to a feed-forward multilayer perceptron with an arbitrary number of layers and makes no assumption about the underlying PDF of the input space. We show a simple unsupervised method by which multidimensional signals can be nonlinearly transformed onto a maximum entropy feature space resulting in statistically independent features
Keywords :
feature extraction; feedforward neural nets; maximum entropy methods; multilayer perceptrons; signal processing; unsupervised learning; arbitrary nonlinear mappings; entropy manipulation; feedforward multilayer perceptron; information theoretic based criterion; maximum entropy feature space; multidimensional signals; statistically independent features; unsupervised learning method; unsupervised method; Entropy; Feature extraction; Feedforward systems; Information theory; Multilayer perceptrons; Mutual information; Neural engineering; Probability density function; Signal mapping; Unsupervised learning;
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
Print_ISBN :
0-7803-4256-9
DOI :
10.1109/NNSP.1997.622379
