Semi-Supervised Feature Selection with Adaptive Discriminant Analysis
2019 ◽
Vol 33
◽
pp. 10083-10084
Keyword(s):
In this paper, we propose a novel Adaptive Discriminant Analysis for semi-supervised feature selection, namely SADA. Instead of computing fixed similarities before performing feature selection, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative method. In each iteration, S is computed from the projected distance with the learned W and W is computed with the learned S. Therefore, SADA can learn better projection matrix W by weakening the effect of noise features with the adaptive similarity matrix. Experimental results on 4 data sets show the superiority of SADA compared to 5 semisupervised feature selection methods.
2018 ◽
Vol 118
◽
pp. 41-51
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Keyword(s):
2013 ◽
Vol 11
(03)
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pp. 1341006
2018 ◽
Vol 8
(3)
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pp. 1805
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Keyword(s):
2011 ◽
pp. 94-108
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2014 ◽
Vol 28
(04)
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pp. 1450009
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2021 ◽
2014 ◽
pp. 280-283
Keyword(s):