Algorithms and Implementation Architectures for Hebbian Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
Theoretical Computer Science
Global Convergence of a PCA Learning Algorithm with a Constant Learning Rate
Computers & Mathematics with Applications
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
Computer Vision and Image Understanding
Neurocomputing
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
A robust and globally convergent PCA learning algorithm
Control and Intelligent Systems
Progressive concept formation in self-organising maps
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Analysis of Hebbian models with lateral weight connections
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A principal components analysis neural gas algorithm for anomalies clustering
WSEAS TRANSACTIONS on SYSTEMS
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Analysis of the sanger hebbian neural network
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A local distribution net for data clustering
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Hi-index | 0.01 |
Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to zero and their local stability. We show how the parameters in the PCA algorithms have to be chosen in order to get an algorithm which converges to a stable equilibrium which provides principal component extraction.