• Publications
  • Influence
A sparse signal reconstruction perspective for source localization with sensor arrays
TLDR
This work presents a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold that has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources. Expand
The Convex Geometry of Linear Inverse Problems
TLDR
This paper provides a general framework to convert notions of simplicity into convex penalty functions, resulting in convex optimization solutions to linear, underdetermined inverse problems. Expand
Rank-Sparsity Incoherence for Matrix Decomposition
TLDR
This paper decomposes a matrix formed by adding an unknown sparse matrix to an unknown low-rank matrix into its sparse and low- rank components. Expand
A shape-based approach to the segmentation of medical imagery using level sets
TLDR
A parametric model for an implicit representation of the segmenting curve is derived by applying principal component analysis to a collection of signed distance representations of the training data to minimize an objective function for segmentation. Expand
MAP estimation via agreement on trees: message-passing and linear programming
TLDR
This work develops and analyze methods for computing provably optimal maximum a posteriori probability (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles and establishes a connection between a certain LP relaxation of the mode-finding problem and a reweighted form of the max-product (min-sum) message-passing algorithm. Expand
A Sticky HDP-HMM With Application to Speaker Diarization
TLDR
An augmented HDP-HMM is described that provides effective control over the switching rate and makes it possible to treat emission distributions nonparametrically, and a sampling algorithm is developed that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence. Expand
A survey of design methods for failure detection in dynamic systems
  • A. Willsky
  • Mathematics, Computer Science
  • Autom.
  • 1 November 1976
TLDR
This paper surveys a number of methods for the detection of abrupt changes in stochastic dynamical systems, focusing on the class of linear systems, but the basic concepts carry over to other classes of systems. Expand
A new class of upper bounds on the log partition function
TLDR
A new class of upper bounds on the log partition function of a Markov random field (MRF) is introduced, based on concepts from convex duality and information geometry, and the Legendre mapping between exponential and mean parameters is exploited. Expand
Analytical redundancy and the design of robust failure detection systems
The failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved byExpand
A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems
We consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times. This model can be used when considering suchExpand
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