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- Samuel Gerber, Tolga Tasdizen, P. Thomas Fletcher, Sarang C. Joshi, Ross T. Whitaker
- Medical Image Analysis
- 2010

This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The manifold… (More)

- Ran Tao, P. Thomas Fletcher, Samuel Gerber, Ross T. Whitaker
- IPMI
- 2009

This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged--so called susceptibility artifacts.… (More)

- Samuel Gerber, Peer-Timo Bremer, Valerio Pascucci, Ross T. Whitaker
- IEEE Transactions on Visualization and Computer…
- 2010

An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy… (More)

- Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ross T. Whitaker
- MICCAI
- 2009

This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the… (More)

- Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker
- ICML
- 2007

Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spectral decomposition, as used for example by the Laplacian Eigenmaps algorithm, provides a powerful tool for non-linear dimensionality reduction and manifold learning. In this paper,… (More)

- Arvind Agarwal, Hal Daumé, Samuel Gerber
- NIPS
- 2010

We present a novel method for multitask learning (MTL) based on manifold regu-larization: assume that all task parameters lie on a manifold. This is the generalization of a common assumption made in the existing literature: task parameters share a common linear subspace. One proposed method uses the projection distance from the manifold to regularize the… (More)

- Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker
- 2009 IEEE 12th International Conference on…
- 2009

We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represented as a parametrized surface represented by a set of parameters that are defined on the input samples. The representation also provides a natural mapping from high to low… (More)

- Kristin C. Potter, Samuel Gerber, Erik W. Anderson
- IEEE Computer Graphics and Applications
- 2013

As dataset size and complexity steadily increase, uncertainty is becoming an important data aspect. So, today's visualizations need to incorporate indications of uncertainty. However, characterizing uncertainty for visualization isn't always straightforward. Entropy, in the information-theoretic sense, can be a measure for uncertainty in categorical… (More)

- Samuel Gerber, Oliver Rübel, Peer-Timo Bremer, Valerio Pascucci, Ross T Whitaker
- Journal of computational and graphical statistics…
- 2013

This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by… (More)

This paper presents a multiscale approach to efficiently compute approximate optimal transport plans between point sets. It is particularly well-suited for point sets that are in high-dimensions, but are close to being intrinsically low-dimensional. The approach is based on an adaptive multiscale decomposition of the point sets. The multiscale decomposition… (More)