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Kernel-based learning algorithms are widely used in machine learning forproblems that make use of the similarity between object pairs. Such algorithmsfirst embed all data points into an alternative space, where the inner productbetween object pairs specifies their distance in the embedding space. Applyingkernel methods to partially labeled datasets is a classical challenge in thisregard, requiring that the distances between unlabeled pairs must somehow belearnt using the labeled data. In this independent study, I will summarize thework of G. Lanckriet et al.'s work on "Learning the Kernel Matrix withSemidefinite Programming" used in support vector machines (SVM) algorithms forthe transduction problem. Throughout the report, I have provide alternativeexplanations / derivations / analysis related to this work which is designed toease the understanding of the original article.

A Summary Of The Kernel Matrix, And How To Learn It Effectively Using Semidefinite Programming