Geodesic
Feature selection algorithm in classification learning using support vector machines
Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 48 (2008) no. 7, pp. 1318-1336

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An algorithm for selecting features in the classification learning problem is considered. The algorithm is based on a modification of the standard criterion used in the support vector machine method. The new criterion adds to the standard criterion a penalty function that depends on the selected features. The solution of the problem is reduced to finding the minimax of a convex-concave function. As a result, the initial set of features is decomposed into three classes – unconditionally selected, weighted selected, and eliminated features. 
@article{ZVMMF_2008_48_7_a14,
     author = {Yu. V. Goncharov and I. B. Muchnik and L. V. Shvartser},
     title = {Feature selection algorithm in classification learning using support vector machines},
     journal = {\v{Z}urnal vy\v{c}islitelʹnoj matematiki i matemati\v{c}eskoj fiziki},
     pages = {1318--1336},
     publisher = {mathdoc},
     volume = {48},
     number = {7},
     year = {2008},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_7_a14/}
}
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Yu. V. Goncharov; I. B. Muchnik; L. V. Shvartser. Feature selection algorithm in classification learning using support vector machines. Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 48 (2008) no. 7, pp. 1318-1336. http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_7_a14/