Abstract
In this work we shall discuss how to apply classical input relevance results for linear Fisher discriminants to measure the relevance of the linear last hidden layer of a Non Linear Discriminant Analysis (NLDA) network. We shall quickly review first possible ways to extend classical and non linear Fisher analysis to multiclass problems and introduce a criterion function very well suited computationally to NLDA networks. After defining a relevance statistic for linear NLDA units, we shall numerically illustrate the resulting procedures on a synthetic 3 class classification problem.
With partial support of Spain’s CICyT, TIC 01-572 and CAM 02-18
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© 2003 Springer-Verlag Berlin Heidelberg
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Dorronsoro, J.R., González, A., Serrano, E. (2003). Linear unit relevance in multiclass NLDA networks. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_23
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DOI: https://doi.org/10.1007/3-540-44868-3_23
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