Independent component analysis: algorithms and applications
Neural Networks
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Independent Component Analysis: A Tutorial Introduction
Independent Component Analysis: A Tutorial Introduction
A New Affine Invariant Curve Normalization Technique Using Independent Component Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Principal Directions-Based Algorithm for Classification Tasks
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
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As it is well known, in nonlinear independent component analysis, usually a series of difficulties related the parameter estimation arise. The reported work proposes an estimation method based on the use of the principal components and on the use of the algorithms of the linear ICA pattern. The estimation of the nonlinear ICA involves a local analysis pattern using the principal directions for the classification of input data and the application of the linear ICA pattern to the level of each class of elements. We use a classification part that corresponds to the nonlinear representation of the mixed data as well as a part of local application of the linear ICA pattern in order to describe the independent characteristics of the data. The purpose is to obtain better data representation as compared to the methods corresponding to the linear ICA pattern at global level. The proposed algorithm was tested in several signals separation applications and the obtained results prove good recognition performances.