Abstract
This work extends the simplified fuzzy ARTMAP (SFAM) to a complex-valued (CV-SFAM) version which is able to work with spatio-temporal data produced in receptive fields of visual cortex. The CV-SFAM’s ability for incremental learning distinguishes CV-SFAM from other complex-valued neural networks, which provides the ability to preserve learned data while learning new samples. We considered different scales and orientations of Gabor wavelets to form a dictionary. This work takes advantage of a locally competitive algorithm (LCA) which calculates more regular sparse coefficients by combining the interactions of artificial neurons. Finally, we provide an experimental real application for biological implementation of sparse dictionary learning to recognize objects in both aligned and non-aligned images.
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Loo, C.K., Memariani, A., Liew, W.S. (2013). A Novel Complex-Valued Fuzzy ARTMAP for Sparse Dictionary Learning. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_45
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DOI: https://doi.org/10.1007/978-3-642-42054-2_45
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