Title :
Unsupervised learning in constrained linear networks
Author :
Palmieri, F. Rancesco ; Zhu, Jie
Author_Institution :
Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA
Abstract :
Constrained linear architectures which learn in unsupervised mode according to Hebb´s rule to minimize the output energy are analyzed. Under which conditions such networks act as decorrelating (square-root) filters is investigated. An algorithm is proposed which is almost optimum since the inputs of each filter (which are also the outputs of the net) become more and more decorrelated as the algorithm progresses. The search essentially approaches Newton´s algorithm. The results of a simulation are shown
Keywords :
learning systems; neural nets; Hebb´s rule; Newton´s algorithm; constrained linear architectures; constrained linear networks; decorrelating filters; output energy minimization; square-root filters; unsupervised learning; Convergence; Decorrelation; Ducts; Filters; Hebbian theory; Intelligent networks; Power engineering and energy; Systems engineering and theory; Unsupervised learning; Vectors;
Conference_Titel :
Bioengineering Conference, 1991., Proceedings of the 1991 IEEE Seventeenth Annual Northeast
Conference_Location :
Hartford, CT
Print_ISBN :
0-7803-0030-0
DOI :
10.1109/NEBC.1991.154555
