Abstract
In this book, we have considered features which are defined by some optimality properties, such as maximum sparseness. In this chapter, we briefly explain how those optimal features can be numerically computed. The solutions are based either on general-purpose optimization methods, such as gradient methods, or specific tailor-made methods such as fixed-point algorithms.
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© 2009 Springer-Verlag London Limited
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Hyvärinen, A., Hurri, J., Hoyer, P.O. (2009). Optimization Theory and Algorithms. In: Natural Image Statistics. Computational Imaging and Vision, vol 39. Springer, London. https://doi.org/10.1007/978-1-84882-491-1_18
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DOI: https://doi.org/10.1007/978-1-84882-491-1_18
Publisher Name: Springer, London
Print ISBN: 978-1-84882-490-4
Online ISBN: 978-1-84882-491-1
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