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Learning the Kernel Matrix with Semidefinite Programming
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. Expand
A new approach to cross-modal multimedia retrieval
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. Expand
Multiple kernel learning, conic duality, and the SMO algorithm
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. Expand
A Robust Minimax Approach to Classification
This work considers a binary classification problem where the mean and covariance matrix of each class are assumed to be known, and addresses the issue of robustness with respect to estimation errors via a simple modification of the input data. Expand
Hilbert Space Embeddings and Metrics on Probability Measures
It is shown that the distance between distributions under γk results from an interplay between the properties of the kernel and the distributions, by demonstrating that distributions are close in the embedding space when their differences occur at higher frequencies. Expand
Semantic Annotation and Retrieval of Music and Sound Effects
We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given aExpand
A Direct Formulation for Sparse PCA Using Semidefinite Programming
A modification of the classical variational representation of the largest eigenvalue of a symmetric matrix is used, where cardinality is constrained, and a semidefinite programming-based relaxation is derived for the sparse PCA problem. Expand
A statistical framework for genomic data fusion
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. Expand
A Direct Formulation for Sparse Pca Using Semidefinite Programming
We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one matrix, with an upper bound on the cardinality of its eigenvector. TheExpand
On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval
A mathematical formulation equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities is proposed, finding that both hypotheses hold, in a complementary form, although evidence in favor of the abstraction hypothesis is stronger than that for correlation. Expand