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检索条件"主题词=Projection algorithms"

共 813 条记录，以下是21-30 订阅

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Distributed mini-batch random projection algorithms for reduced communication overhead

Distributed mini-batch random projection algorithms for redu...

引用

IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Soomin Lee Angelia Nedić Electrical and Computer Engineering University of Illinois Urbana IL USA

We propose a gossip-based mini-batch random projection (GMRP) algorithm that can reduce communication overhead for a distributed optimization problem defined over a network with a very large number of constraints. We state a convergence result and provide an application of the GMRP, text classification with support vector machines.

关键词： Optimization Convergence Vectors projection algorithms Training Educational institutions Support vector machines

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Fast projection algorithms for Level Set Evolution in Potts Model

Fast Projection Algorithms for Level Set Evolution in Potts ...

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International Conference on Computer Science and Network Technology

作者： Jing WANG Zhenkuan PAN Yongguo ZHENG Jinming DUAN College of Information Science and Engineering Shandong University of Science and Technology College of Information Engineering Qingdao University

ISBN: (纸本)9781467329637

Potts model is a basic variational model for multiphase image segmentation. Under the variational level set framework, the model is usually implemented by solving a gradient descent equation derived from energy functional minimization together with re-initialization process to preserve the level set function as a signed distance function. Because of the existence of complicated curvature terms in the PDE equations and the time-consuming re-initialization process, this method is with low computation efficiency. In this paper, we combine the Split Bregman algorithm, Dual method together with a very simple projection method to overcome the problems mentioned above. We name these two methods as Split Bregman projection Method (SBPM) and Dual Split Bregman projection Method (DSBPM). They naturally preserve the level set function as a signed distance function during the evolution without the initialization process. These methods are compared with traditional method and the ones proposed by other authors according to some numerical experiments. These examples demonstrate that our proposed methods are of higher efficiency than the other ones and can segment images quickly.

关键词： Potts model Variational level set method Multiphase image segmentation Split Bregman projection Method Dual Split Bregman projection Method Potts model distance function projection method Level set reinitialization evolution projection algorithms Evolution image segmentation

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Spherical subspace and eigen based affine projection algorithms

Spherical subspace and eigen based affine projection algorit...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： R.D. DeGroat D. Begusic E.M. Dowling D.A. Linebarger Dept. of Electr. Eng. Texas Univ. Dallas TX USA FESB Split University of Split Split Croatia Electrical Engineering University of Technology Richardson TX USA

We combine spherical subspace (SS) and eigen based updating methods with the affine projection (AP) method to produce a new family of fast SS-AP algorithms that offers additional tradeoffs between computation and adaptive filtering performance. Moreover, the implementation of SS-AP is less complicated than the fast RLS based AP algorithms. For certain applications, e.g., echo cancellation and equalization in digital subscriber loop (DSL) transceivers, SS-AP offers performance that is comparable to AP, but at computational costs that are less than the fast AP algorithms.

关键词： projection algorithms Resonance light scattering Computational efficiency Filtering algorithms Adaptive filters Costs Eigenvalues and eigenfunctions DSL Convergence Telecommunication computing

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Generating Optimum Number of Clusters Using Median Search and projection algorithms

Generating Optimum Number of Clusters Using Median Search an...

引用

International Conference on Advances in Computer Engineering (ACE)

作者： Suresh L. Jay B. Simha Rajappa Veluru Abiba Systems Bangalore India CSE Dept. CI Tech Bangalore India

ISBN: (纸本)9781424471546;9780769540580

K-means Clustering is an important algorithm for identifying the structure in data. In this work, a novel approach to seeding the clusters with the latent data structure is proposed. This is expected to minimize: the need for number of clusters apriory and time for convergence by providing near optimal cluster centers. Also these algorithms are tested on the latest standards for data warehouses - the column store databases.

关键词： projection algorithms Clustering algorithms Convergence Testing Databases Computer architecture Shape measurement Data structures Data warehouses Programming profession

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Distributed signal subspace projection algorithms with maximum convergence rate for sensor networks with topological constraints

Distributed signal subspace projection algorithms with maxim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： S. Barbarossa G. Scutari T. Battisti INFOCOM Department University of Roma La Sapienza Rome Italy

The observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. Global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. However, centralized networks are more vulnerable and prone to congestion around the sink nodes. To relax the congestion problem, decrease the network vulnerability and improve the network efficiency, it is appropriate to bring the decisions at the lowest possible level. In this paper, we propose a distributed algorithm allowing each node to improve the reliability of its own reading thanks to the interaction with the other nodes, assuming that the field monitored by the network is a smooth function. In mathematical terms, this only requires that the useful field belongs to a subspace of dimension smaller than the number of nodes. Although fully decentralized, the proposed algorithm is globally optimal, in the sense that it performs the projection of the overall set of observations onto the signal subspace through an iterative decentralized algorithms, that requires minimum convergence time, for any given node coverage.

关键词： projection algorithms Convergence Subspace constraints Distributed algorithms Iterative algorithms Battery charge measurement Monitoring Pollution measurement Energy consumption Noise level

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DrSVM: Distributed Random projection algorithms for SVMs

DrSVM: Distributed Random Projection Algorithms for SVMs

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IEEE Annual Conference on Decision and Control

作者： Soomin Lee Angelia Nedic Electrical and Computer Engineering University of Illinois Industrial and Enterprise Systems Engineering University of Illinois

ISBN: (纸本)9781467320658

We present distributed random projected gradient algorithms for Support Vector Machines (SVMs) that can be used by multiple agents connected over a time-varying network. The goal is for the agents to cooperatively find the same maximum margin hyperplane. In the primal SVM formulation, the objective function can be represented as a sum of convex functions and the constraint set is an intersection of multiple halfspaces. Each agent minimizes a local objective subject to a local constraint set. It maintains its own estimate sequence and communicates with its neighbors. More specifically, each agent calculates weighted averages of the received estimates and its own estimate, adjust the estimate by using gradient information of its local objective function and project onto a subset of its local constraint set. At each iteration, an agent considers only one halfspace since projection onto a single halfspace is easy. We also consider the convergence behavior of the algorithms and prove that all the estimates of agents converge to the same limit point in the optimal set.

关键词： Constraint Set Agents Support Vector Network projection algorithms Convex function Objective function Time varying networks

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Selective-partial-update NLMS and affine projection algorithms for acoustic echo cancellation

Selective-partial-update NLMS and affine projection algorith...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： K. Dogancay O. Tanrikulu Sch. of Phys. & Electron. Syst. Eng. Univ. of South Australia Mawson Lakes SA Australia

Adaptive filters used in acoustic echo cancellation often require a large number of coefficients to model the acoustic echo path with sufficient accuracy. The computational complexity of adaptation algorithms such as the NLMS algorithm is proportional to the filter length, which means that for long filters the adaptation task can become prohibitively expensive. The purpose of partial coefficient update is to reduce the computational complexity by adapting a subset of the filter coefficients at every iteration. In this paper we develop selective-partial-update NLMS and AP algorithms based on the principle of minimum disturbance. The algorithms are based on sound theoretical justification and appear to have good convergence performance as attested to by computer simulations with real speech signals.

关键词： projection algorithms Echo cancellers Finite impulse response filter Adaptive filters Computational complexity Speech Lagrangian functions Physics Acoustical engineering Systems engineering and theory

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Adaptive eigen-projection algorithms for 1-D and 2-D antenna arrays

Adaptive eigen-projection algorithms for 1-D and 2-D antenna...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： J.A. Foutz A.S. Spanias Department of Electrical Engineering Telecommunications Research Center Arizona State University Tempe AZ USA

In this paper, we develop 1-dimensional (1-D) and 2-dimensional (2-D) adaptive algorithms that use gradient projections in select sub-spaces for use in antenna beamforming. The 1-D algorithm is essentially a significantly enhanced version of an eigen-projection algorithm that was previously developed for system identification. The 2-D algorithm is based on a stacked configuration of the enhanced 1-D algorithm. Both algorithms are applied to antenna beamforming of uniform linear and planar arrays. Simulation results show better convergence as well as improved beam properties.

关键词： Adaptive arrays Antenna arrays Planar arrays projection algorithms Adaptive algorithm Array signal processing Convergence System identification Linear antenna arrays Eigenvalues and eigenfunctions

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Cooperative sensing for cognitive radio using decentralized projection algorithms

Cooperative sensing for cognitive radio using decentralized ...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Sergio Barbarossa Gesualdo Scutari Timothy Battisti INFOCOM Department University of Roma La Sapienza Rome Italy

Sensing the radio spectrum is an essential feature of cognitive radio. What is mostly important for a cognitive transmitter is to know the power spectral density at the location of its intended receiver and of the primary receivers. This requires, as a whole, knowledge of the spatial distribution of the power spectral density, an information that could be delivered by a network of sensors distributed over the territory, where each node estimates the local power spectral density and sends this information to a control node. The criticalities of this strategy are the limited sensing capabilities of the single node, shadowing effects and potential congestion around the sink nodes collecting all the estimated wideband spectra. In this paper, we propose a fully decentralized iterative algorithm allowing the network to project the measured spatial spectral density onto the useful signal subspace with the minimum convergence time. The idea is based on the assumption, typically valid in practice, that the spatial distribution of the power spectral density, for each frequency, is mostly concentrated over a vector space of dimension much smaller than the number of sensing nodes. The proposed technique yields a considerable reduction of estimation noise and undesired shadowing effects, without requiring the presence of a centralized control node. Interestingly, we show that in our set-up, the selection of the signal subspace dimension depends not only on the bias error and on the noise variance, as in any MSE algorithm, but also on the transmit power available at each node.

关键词： Cognitive radio projection algorithms Receivers Shadow mapping Radio transmitters Wideband Iterative algorithms Density measurement Time measurement Convergence

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Complex-valued FIR seismic migration filter design using Pure and Relaxed projection algorithms

Complex-valued FIR seismic migration filter design using Pur...

引用

European Signal Processing Conference (EUSIPCO)

作者： Wail A. Mousa Said Boussakta Des McLernon School of Electronic & Electrical Engineering The University of Leeds Leeds United Kingdom

The iterative Pure and Relaxed Projectors are special cases of Vector Space projection Methods (VSPM). Complex-valued FIR digital filter design using Pure and Relaxed Projectors is proposed for designing seismic migration digital filters. Designing FIR digital filters using VSPM, in general, is able to produce feasible solutions satisfying all the desired filter constraints by the use of only two Fast Fourier Transform (FFT) computations. Furthermore, the design algorithm can be directly extended from one-dimensional (1-D) to multi-dimensional (m-D) filters, an advantage not present in many other filter design techniques. In addition, by using the relaxed projectors, one can achieve faster convergence when compared to the pure projectors. The simulation results show that the relaxed projectors for designing such filters will save up to 86.47% of the number of iterations when compared with the pure projectors.

关键词： Finite impulse response filters Algorithm design and analysis projection algorithms Vectors Passband Filtering algorithms Hilbert space

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