Transcription of Independent Component Analysis: Algorithms and Applications
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Independent Component Analysis: Algorithms and Applications
Independent Component Analysis: Algorithms and ApplicationsAapo Hyv rinen and Erkki OjaNeural Networks Research CentreHelsinki University of Box 5400, FIN-02015 HUT, FinlandNeural Networks, 13(4-5):411-430, 2000 AbstractA fundamental problem in neural network research, as well asin many other disciplines, is finding a suitablerepresentation of multivariate data, random reasons of computational and conceptual simplicity,the representation is often sought as a linear transformation of the original data. In other words, each componentof the representation is a linear combination of the original variables. Well-known linear transformation methodsinclude principal Component analysis, factor analysis, and projection pursuit. Independent Component analysis(ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data sothat the components are statistically Independent , or as Independent as possible.
Another, very different application of ICA is on feature extraction. A fundamental problem in digital signal processing is to find suitable representations for image, au dio or other kind of data for tasks like compression and denoising. Data representations are often based on (discrete) linear transformations. Standard linear transforma-
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