source separation and density estimation
Source Separation and Density Estimation by Faithful Equivariant SOM
Our modifications of the self-organizing map (SOM) algorithm results in purely digital learning rules which perform non-parametric his(cid:173) togram density estimation. The non-parametric nature of the sep(cid:173) aration allows for source separation of non-linear mixtures. An anisotropic coupling is introduced into our SOM with the role of aligning the network locally with the independent component con(cid:173) tours. This approach provides an exact verification condition for source separation with no prior on the source distributions.
Source Separation and Density Estimation by Faithful Equivariant SOM
Lin, Juan K., Cowan, Jack D., Grier, David G.
To appear in Neural Computation.
Source Separation and Density Estimation by Faithful Equivariant SOM
Lin, Juan K., Cowan, Jack D., Grier, David G.
To appear in Neural Computation.
Source Separation and Density Estimation by Faithful Equivariant SOM
Lin, Juan K., Cowan, Jack D., Grier, David G.
To appear in Neural Computation.