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Line 101: [[Unsupervised learning\|Unsupervised]] multiple kernel learning algorithms have also been proposed <ref>J. Zhuang, J. Wang, S.C.H. Hoi & X. Lan. [http://jmlr.csail.mit.edu/proceedings/papers/v20/zhuang11/zhuang11.pdf Unsupervised Multiple Kernel Learning]. Jour. Mach. Learn. Res. 20:129–144, 2011</ref>. The problem is defined as follows. Let <math>U={x_i}</math> be a set of unlabeled data. The kernel definition is the linear combined kernel <math>K'=\sum_{i=1}^M\beta_iK_m</math>. The minimization problem can be written as follows. :<math>\min_f\sum^n_{i=1}\left\Vert{ x_i - \sum_{x_j} K(x_i,x_j)x_j\right\Vert}^ 2</math> These algorithms seek to optimize the kernel using the following principles:

Multiple kernel learning: Difference between revisions