Scalable Low-rank Matrix and Tensor Decomposition on Graphs
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We compare and contrast from a geometric perspective a number of low-dimensional signal models that support stable information-preserving dimensionality reduction. We consider sparse and compressible signal models for deterministic and random signals, stru ...
This paper presents a compact model for LINC (linear amplification with non linear components) transmitters an their power combiners. The study focuses on the detrimental effect of the transmission line nonidealities. A mathematical description of the syst ...
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In this paper we propose a novel information theoretic criterion for optimizing the linear combination of classifiers in multi stream automatic speech recognition. We discuss an objective function that achieves a trade-off between the minimization of a bou ...
Most state-of-the-art algorithms for filament detection in 3-D image-stacks rely on computing the Hessian matrix around individual pixels and labeling these pixels according to its eigenvalues. This approach, while very effective for clean data in which li ...
We give a convergence estimate for a Petrov-Galerkin Algebraic Multigrid method. In this method, the prolongations are defined using the concept of smoothed aggregation while the restrictions are simple aggregation operators. The analysis is carried out by ...
LTCC (Low-Temperature Cofired Ceramic) has attracted considerable interest as a material for sensors and microfluidic circuits, for which dimensional accuracy is essential. The irregular shrinkage behaviour of LTCC tapes must be taken into account when des ...
A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analys ...
This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the proposed techniques is that they attempt to preserve both the intrinsic neighborhood geometry of the data samples and the global geome ...
We propose an approximation framework for distributed target localization in sensor networks. We represent the unknown target positions on a location grid as a sparse vector, whose support encodes the multiple target locations. The location vector is linea ...
