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where . One formulation of this is defined as follows. Let <math>D\in {0,1}^{n\times n}</math> be a matrix such that <math>D_{ij}=1</math> means that <math>x_i</math> and <math>x_j</math> are neighbors. Then, <math>B_i={x_j:D_{ij}=1}</math>. Note that these groups must be learned as well. Zhuang et al. solve this problem by an alternating minimization method for <math>K</math> and the groups <math>B_i</math>. For more information, see Zhuang et al.<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>
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Available MKL libraries include
* [http://www.cs.cornell.edu/~ashesh/pubs/code/SPG-GMKL/download.html SPG-GMKL]: A scalable C++ MKL SVM library that can handle a million kernels.<ref>Ashesh Jain, S. V. N. Vishwanathan and Manik Varma. SPG-GMKL: Generalized multiple kernel learning with a million kernels. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, August 2012</ref>
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