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Independent component analysis: algorithms and applications
Independent Component Analysis
In this chapter, we discuss a statistical generative model called independent component analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse coding. It shows…
Simplified neuron model as a principal component analyzer
- E. Oja
- BiologyJournal of mathematical biology
A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to…
A Fast Fixed-Point Algorithm for Independent Component Analysis
A novel fast algorithm for independent component analysis is introduced, which can be used for blind source separation and feature extraction, and the convergence speed is shown to be cubic.
A new curve detection method: Randomized Hough transform (RHT)
Subspace methods of pattern recognition
- E. Oja
- Computer Science
Neural Networks, Principal Components, and Subspaces
- E. Oja
- Computer ScienceInt. J. Neural Syst.
A single neuron with Hebbian-type learning for the connection weights, and with nonlinear internal feedback, has been shown to extract the statistical principal components of its stationary input pattern sequence, which yields a multi-dimensional, principal component subspace.
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
Experimental results show that RPCL outperforms FSCL when used for unsupervised classification, for training a radial basis function (RBF) network, and for curve detection in digital images.
On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
Independent component approach to the analysis of EEG and MEG recordings
- R. Vigário, J. Särelä, V. Jousmiki, M. Hämäläinen, E. Oja
- Biology, Computer ScienceIEEE Transactions on Biomedical Engineering
- 1 May 2000
ICA has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic and magnetoencephalographical recordings and has been applied to the analysis of brain signals evoked by sensory stimuli.