By Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh
This publication is either a reference for engineers and scientists and a instructing source, that includes educational chapters and study papers on function extraction. Its CD-ROM comprises the knowledge of the NIPS 2003 characteristic choice problem and pattern Matlab® code. beforehand there was inadequate attention of function choice algorithms, no unified presentation of best equipment, and no systematic comparisons.
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Extra resources for Feature Extraction: Foundations and Applications
In Figures 2-g and h, we show an example in three dimensions illustrating diﬀerences of the forward and backward selection processes. In this example, a forward selection method would choose ﬁrst x3 and then one of the two other features, yielding to one of the orderings x3 , x1 , x2 or x3 , x2 , x1 . A backward selection method would eliminate x3 ﬁrst and then one of the two other features, yielding to one of the orderings x1 , x2 , x3 or x2 , x1 , x3 . Indeed, on Figure 2-h, we see that the front projection in features x1 and x2 gives a ﬁgure similar to Figure 2-e.
G. (Kohavi, 1995)).
N. Tishby, F. C. Pereira, and W. Bialek. The information bottleneck method. In Proc. of the 37th Annual Allerton Conference on Communication, Control and Computing, pages 368–377, 1999. J. S. Walker. A primer on wavelets and their scientiﬁc applications. Chapman and Hall/CRC, 1999. P. M. Karp. Cliﬀ: Clustering of high-dimensional microarray data via iterative feature ﬁltering using normalized cuts. In 9th International Conference on Intelligence Systems for Molecular Biology, 2001. 24 Isabelle Guyon and Andr´e Elisseeﬀ A Forward Selection with Gram-Schmidt Orthogonalization function idx = gram_schmidt(X, Y, featnum) %idx = gram_schmidt(X, Y, featnum) % Feature selection by Gram Schmidt orthogonalization.