Abstract
Using a recent algorithm for non linear mapping, Curvilinear Component Analysis, we show through three applications how a priori knowledge can be introduced in the CCA framework, and we translate this knowledge in term of mapping constraints. This a priori knowledge can be introduced to constraint the convergence of the algorithm toward a data structure having a best interpretation according to the physical process of input data generation. The three applications concern geographical data representation, speech recognition and IRMf image processing.
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Guérin-Dugué, A., Teissier, P., Gafaro, G.D., Hérault, J. (1999). Curvilinear Component Analysis for high-dimensional data representation: II. Examples of additional mapping constraints in specific applications . In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100531
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DOI: https://doi.org/10.1007/BFb0100531
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