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LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING
2000
International Journal of Neural Systems
In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper a new structure is proposed, where local ICA models are used in connection with a suitable grouping algorithm clustering the data. The clustering part is responsible for an overall coarse nonlinear representation of the
doi:10.1142/s0129065700000429
pmid:11307858
fatcat:hfayqbfhajhkzn4qnvbfjjaoxa