Titlebook: Non-Standard Parameter Adaptation for Exploratory Data Analysis; Wesam Ashour Barbakh,Ying Wu,Colin Fyfe Book 2009 Springer-Verlag Berlin

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Reinforcement Learning of Projections,e show that the last method has accurate convergence, even for non-linear projections..Also, it is frequently important in projection methods to identify multiple components. Although we can find more than one component by deflationary methods such as the Gram-Schmidt method, these methods seem to b

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1860-949X elation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combi978-3-642-26055-1978-3-642-04005-4Series ISSN 1860-949X Series E-ISSN 1860-9503

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Book 2009tion of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algo

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Review of Linear Projection Methods,ered data space by projecting the data to a lower dimensional space. The basic idea is to find some suitable function ., which maps the original data sample . into a .-dimensional manifold by .(.)?=?., where .. In this section, we review several projection methods in detail.

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