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Gert R. G. Lanckriet
Researcher at University of California, San Diego
Publications - 139
Citations - 19369
Gert R. G. Lanckriet is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Support vector machine & Music information retrieval. The author has an hindex of 58, co-authored 139 publications receiving 17897 citations. Previous affiliations of Gert R. G. Lanckriet include University of California, Los Angeles & University of California.
Papers
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Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
Proceedings ArticleDOI
Multiple kernel learning, conic duality, and the SMO algorithm
TL;DR: Experimental results are presented that show that the proposed novel dual formulation of the QCQP as a second-order cone programming problem is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.
Proceedings ArticleDOI
A new approach to cross-modal multimedia retrieval
Nikhil Rasiwasia,Jose Costa Pereira,Emanuele Coviello,Gabriel Doyle,Gert R. G. Lanckriet,Roger Levy,Nuno Vasconcelos +6 more
TL;DR: It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy and are shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.
Journal ArticleDOI
A statistical framework for genomic data fusion
TL;DR: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins.
Journal ArticleDOI
A Direct Formulation for Sparse PCA Using Semidefinite Programming
TL;DR: In this paper, the authors consider the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficients in this combination.