LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING

JUHA KARHUNEN, SIMONA MĂlĂROIU, MIKA ILMONIEMI
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
more » ... ta, while linear ICA models of each cluster are used for describing local features of the data. The goal is to represent the data better than in linear ICA while avoiding computational di culties related with nonlinear ICA. Several data grouping methods are considered, including standard K-means clustering, selforganizing maps, and neural gas. Connections to existing methods are discussed, and experimental results are given for arti cial data and natural images. Furthermore, a general theoretical framework encompassing a large number of methods for representing data is introduced. These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.
doi:10.1142/s0129065700000429 pmid:11307858 fatcat:hfayqbfhajhkzn4qnvbfjjaoxa