Of course, the count function should be fixed.

The random features method, proposed by Rahimi and Recht [2008], maps the data to a finite dimensional feature space as a random approximation to the feature space of RBF kernels. With explicit finite dimensional feature vectors available, the original KSVM is converted to a linear support vector machine (LSVM), that can be trained by faster algorithms (Shalev-Shwartz et al.

Several approaches have been proposed to remedy the edacious nature of archetypal analysis, proposing, e.g.,efficient active-set quadratic programming (Chen etal.,2014),

Models that are learned from real-world data are often biased because the data usedtotrainthemisbiased.

Hereθi are training data points whereLc(θi)has already been evaluated.

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