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- David P. Wipf, Bhaskar D. Rao
- IEEE Transactions on Signal Processing
- 2004

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we… (More)

- David P. Wipf, Bhaskar D. Rao
- IEEE Transactions on Signal Processing
- 2007

Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent L>1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were putatively generated by the same small subset of features.… (More)

- David P. Wipf, Srikantan S. Nagarajan
- NIPS
- 2007

Automatic relevance determination (ARD) and the closely-related sparse Bayesian learning (SBL) framework are effective tools for pruning large numbers of irrelevant features leading to a sparse explanatory subset. However, popular update rules used for ARD are either difficult to extend to more general problems of interest or are characterized by non-ideal… (More)

- David P. Wipf, Bhaskar D. Rao, Srikantan S. Nagarajan
- IEEE Transactions on Information Theory
- 2011

Many practical methods for finding maximally sparse coefficient expansions involve solving a regression problem using a particular class of concave penalty functions. From a Bayesian perspective, this process is equivalent to maximum a posteriori (MAP) estimation using a sparsity-inducing prior distribution (Type I estimation). Using variational techniques,… (More)

We consider criteria for variational representations of non-Gaussian latent variables, and derive variational EM algorithms in general form. We establish a general equivalence among convex bounding methods, evidence based methods, and ensemble learning/Variational Bayes methods, which has previously been demonstrated only for particular cases.

- David P. Wipf, Srikantan S. Nagarajan
- NeuroImage
- 2009

The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions.… (More)

- Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa
- 2012 IEEE Conference on Computer Vision and…
- 2012

This paper presents a robust photometric stereo method that effectively compensates for various non-Lambertian corruptions such as specularities, shadows, and image noise. We construct a constrained sparse regression problem that enforces both Lambertian, rank-3 structure and sparse, additive corruptions. A solution method is derived using a hierarchical… (More)

- David P. Wipf, Haichao Zhang
- EMMCVPR
- 2013

- Xudong Cao, David P. Wipf, Fang Wen, Genquan Duan, Jian Sun
- 2013 IEEE International Conference on Computer…
- 2013

Face verification involves determining whether a pair of facial images belongs to the same or different subjects. This problem can prove to be quite challenging in many important applications where labeled training data is scarce, e.g., family album photo organization software. Herein we propose a principled transfer learning approach for merging plentiful… (More)

- David P. Wipf, Julia P. Owen, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan
- NeuroImage
- 2010

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and… (More)