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- Seung-Jean Kim, K. Koh, Mike Lustig, S. Boyd, D. M. Gorinevsky
- IEEE Journal of Selected Topics in Signalâ€¦
- 2007

Recently, a lot of attention has been paid to regularization based methods for sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) and feature selection (e.g., theâ€¦ (More)

- Kwangmoo Koh, Seung-Jean Kim, Stephen P. Boyd
- Journal of Machine Learning Research
- 2007

Logistic regression with `1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method forâ€¦ (More)

Recently, a lot of attention has been paid to l1 regularization based methods for sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) and feature selection (e.g., theâ€¦ (More)

- Stephen Boyd, Arpita Ghosh, +4 authors Jun Sun
- 2006

We consider the problem of choosing the edge weights of an undirected graph so as to maximize or minimize some function of the eigenvalues of the associated Laplacian matrix, subject to someâ€¦ (More)

- Stephen P. Boyd, Seung-Jean Kim, Dinesh Patil, Mark Horowitz
- Operations Research
- 2005

This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program (GP) or generalized geometric program (GGP), which can be transformed to a convexâ€¦ (More)

- Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd
- ICML
- 2006

In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends onâ€¦ (More)

- Seung-Jean Kim, Kwangmoo Koh, Stephen P. Boyd, Dimitry M. Gorinevsky
- SIAM Review
- 2009

The problem of estimating underlying trends in time series data arises in a variety of disciplines. In this paper we propose a variation on Hodrickâ€“Prescott (H-P) filtering, a widely used method forâ€¦ (More)

- Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd
- NIPS
- 2005

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of dataâ€¦ (More)

- Joong Ho Won, Seung-Jean Kim
- 2006 Fortieth Asilomar Conference on Signalsâ€¦
- 2006

In many signal processing applications, we want to estimate the covariance matrix of a multivariate Gaussian distribution. We often require the estimate to be not only invertible but alsoâ€¦ (More)

The optimal solution of a geometric program (GP) can be sensitive to variations in the problem data. Robust geometric programming can systematically alleviate the sensitivity problem by explicitlyâ€¦ (More)