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Künstliche Intelligenz 26(4) - November 2012

https://dl.gi.de/handle/20.500.12116/11109
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Auflistung Künstliche Intelligenz 26(4) - November 2012 nach Schlagwort "Blind source separation"

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  • Zeitschriftenartikel
    Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Escalante-B., Alberto N.; Wiskott, Laurenz
    Slow Feature Analysis (SFA) is an unsupervised learning algorithm based on the slowness principle and has originally been developed to learn invariances in a model of the primate visual system. Although developed for computational neuroscience, SFA has turned out to be a versatile algorithm also for technical applications since it can be used for feature extraction, dimensionality reduction, and invariance learning. With minor adaptations SFA can also be applied to supervised learning problems such as classification and regression. In this work, we review several illustrative examples of possible applications including the estimation of driving forces, nonlinear blind source separation, traffic sign recognition, and face processing.
  • Zeitschriftenartikel
    Sparse Coding and Selected Applications
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hocke, Jens; Labusch, Kai; Barth, Erhardt; Martinetz, Thomas
    Sparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to generalize Neural Gas to successfully learn sparse coding dictionaries. We explore applications of sparse coding to image-feature extraction, image reconstruction and deconvolution, and blind source separation.