G. Lanckriet

Sparse Principal Component Analysis using Semidefinite Programming (2009)

L. El Ghaoui, M. Jordan, G. Lanckriet

• Classical dimensionality reduction tool. • Numerically cheap: O(kn 2), only requires computing k leading eigenvectors. Sparse PCA • Seeks factors with a few nonzero coefficients.

A direct formulation for sparse PCA using semidefinite programming (2009)

L. El Ghaoui, M. Jordan, G. Lanckriet

programming

Sparse PCA with applications in finance (2008)

L. El Ghaoui, M. Jordan, G. Lanckriet

Principal Component Analysis (PCA): classic tool in multivariate data analysis • Input: a covariance matrix A • Output: a sequence of factors ranked by variance

Sparse PCA with applications in finance (2008)

L. El Ghaoui, M. Jordan, G. Lanckriet

Principal Component Analysis (PCA): classic tool in multivariate data analysis • Input: a covariance matrix A • Output: a sequence of factors ranked by variance

Sparse PCA with applications in finance (2008)

L. El Ghaoui, M. Jordan, G. Lanckriet

Principal Component Analysis (PCA): classic tool in multivariate data analysis • Input: a covariance matrix A • Output: a sequence of factors ranked by variance

Injective Hilbert Space Embeddings of Probability Measures (2008)

Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Lanckriet, G., Schölkopf, B.

A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. This embedding...

Injective Hilbert Space Embeddings of Probability Measures (2008)

Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Lanckriet, G., Schölkopf, B., Servedio, R. A., ...

A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. This embedding...

A direct formulation for sparse PCA using semidefinite programming (2004)

L. El Ghaoui, M. Jordan, G. Lanckriet

Principal Component Analysis (PCA): classic tool in multivariate data analysis • Input: a covariance matrix A • Output: a sequence of factors ranked by variance

Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis (2002)

Lanckriet, G, Lambrechts, A

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the...

Multiclass LS-SVMs: Moderated outputs and coding-decoding schemes (2002)

Lanckriet, G, Lambrechts, A

A common way of solving the multiclass categorization problem is to reformulate the problem into a set of binary classification problems. Discriminative binary classifiers like, e.g., Support Vector...

Financial time series prediction using least squares support vector machines within the evidence framework (2001)

Baestaens, DE, Lambrechts, A, Lanckriet, G, ...

For financial time series, the generation of error bars on the point prediction is important in order to estimate the corresponding risk. The Bayesian evidence framework, already successfully applied...

Bayesian interpretation of least squares support vector machines for financial time series prediction (2001)

Van Gestel, T, Lanckriet, G, Lambrechts, A, Baestaens, D, ...

\emph{Proc. of the 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001)}, Orlando, Florida, Jul. 2001