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Elis research interests include hybrid generative discriminative learning and probabilistic graphical model.
There are people who have deficits in conditional discriminative learning and, therefore, simple tasks that require this type of discriminations may be a challenge for them.
j]) is to exploit label information when learning the generative model [theta], which is essentially a discriminative learning approach (supervised) for generative model as well as FFSS, differing from the native FFSS where the label information is absent (unsupervised).
The canonical correlations of within-class sets and between-class sets for discriminative learning have been explored, and some [24] have used canonical correlation analysis for feature fusion by determining pairs of projective matrices, given two candidate features.