Collaborative filtering (CF) is an important technique used in some recommendation *** task of CF is to estimate the persons' preferences (e.g.,ratings) or to predict the preferences for the future,based on some a...
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Collaborative filtering (CF) is an important technique used in some recommendation *** task of CF is to estimate the persons' preferences (e.g.,ratings) or to predict the preferences for the future,based on some already known persons' *** general,the model-based CF performs better than the memory-based CF,especially for highly sparse *** this paper,we present a new model-based CF method for bounded support data,which takes into account the facts that the ratings are usually in a limited interval.A nonnegative matrix factorization (NMF) model is applied to investigate and learn the patterns hidden in the observed data *** rating value is assumed to be beta distributed and we assign the gamma prior to the parameters in a beta distribution for the purpose of Bayesian *** variation inference framework and some lower bound approximations,an analytically tractable solution can be obtained for the proposed NMF *** comparing with several existing low-rank matrix approximation methods,the good performance of the proposed method is demonstrated.
Branch and Bound (B&B) algorithms are highly parallelizable but they are irregular and dynamic load balancing techniques have been used to avoid idle processors. In previous work, authors use a dynamic number of t...
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ISBN:
(纸本)9780769543284
Branch and Bound (B&B) algorithms are highly parallelizable but they are irregular and dynamic load balancing techniques have been used to avoid idle processors. In previous work, authors use a dynamic number of threads at run time, which depends on the measured performance of the application for just one interval B&B algorithm running on the system. In this way, load balancing is achieved by thread generation decisions. In this work, we extend the study of these models to non-dedicated systems. In order to have a controlled testbed and comparable results, several instances of the interval global optimization algorithm are executed in the system, with the same model and problem to solve. Therefore, a non-dedicated system is simulated because the execution of one application affects the execution of the other instances. This paper discusses different methods and models to decide when a thread should be created. Experiments show which of the proposed methods performs best in terms of maximum running time per application, using the fewest running threads. Following this parallel programming methodology, which is well suited for other B&B codes, applications can adapt their parallelism level to their performance and load of the system (at run time). This work represents a step forward towards increasing the performance of parallel algorithm running in non-dedicated and heterogeneous systems. The adaptive model discussed in this work is able to reduce the overall execution time for a set of instances of the same application running simultaneously. It also exempts the user from specifying the number of threads each application should use.
We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approxima...
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We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approximate solutions of hard problems. An extensive set of experiments are performed for a variety of application problems including graph cut segmentation, curvature regularization and more generally the optimization of MRFs. We demonstrate that the technique can be useful for desktop computers, graphical processing units and supercomputer clusters. To facilitate further research, an implementation of the decomposition methods is made publicly available. (C) 2011 Elsevier Inc. All rights reserved.
Stereo matching is an active area of research in imageprocessing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimat...
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ISBN:
(纸本)9781457705397
Stereo matching is an active area of research in imageprocessing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimation problem and propose to solve it in a more general convex optimization framework based on proximal methods. More precisely, unlike previous works where the criterion must satisfy some restrictive conditions in order to be able to numerically solve the minimization problem, this work offers a great flexibility in the choice of the involved criterion. The method is validated in a stereo image coding framework, and the results demonstrate the good performance of the proposed parallel proximal algorithm.
Smoothing filter is the method of choice for image preprocessing and pattern recognition. We present a new concurrent method for smoothing 2D object in binary case. Proposed method provides a parallel computation whil...
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ISBN:
(纸本)9780819484086
Smoothing filter is the method of choice for image preprocessing and pattern recognition. We present a new concurrent method for smoothing 2D object in binary case. Proposed method provides a parallel computation while preserving the topology by using homotopic transformations. We introduce an adapted parallelization strategy called split, distribute and merge (SDM) strategy which allows efficient parallelization of a large class of topological operators including, mainly, smoothing, skeletonization, and watershed algorithms. To achieve a good speedup, we cared about task scheduling. distributed work during smoothing process is done by a variable number of threads. Tests on 2D binary image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2x Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 5.2 thus a cadency of 32 images per second is achieved.
Fractal image coding is one of the most prominent compression technologies. It can be also used for industrial applications like image indexing methods and image retrieval methods. On the other hand, GPGPU (General Pu...
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ISBN:
(纸本)9781457704345
Fractal image coding is one of the most prominent compression technologies. It can be also used for industrial applications like image indexing methods and image retrieval methods. On the other hand, GPGPU (General Purpose computing on Graphic processing Unit) attracts a great deal of attention, which is used for general-purpose computations like numerical calculations as well as graphic processing. In this paper, we evaluate two parallel programs for adaptive fractal image coding algorithms on GPUs by using CUDA (Compute Unified Device Architecture) and and discuss the effectiveness of parallel programs using index vectors. The adaptive approach can achieve the image compression with given quality level by changing the size of range blocks in any location.
The development of parallel implementations is an important task for hyperspectral data exploitation. In most cases, real-time or nearly real-time processing of hyperspectral images is required for swift decisions whi...
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ISBN:
(纸本)9780769545769
The development of parallel implementations is an important task for hyperspectral data exploitation. In most cases, real-time or nearly real-time processing of hyperspectral images is required for swift decisions which depend upon high computing performance of algorithm analysis. A popular algorithm in hyperspectral image interpretation is the automatic target generation process (ATGP). In this paper, we develop a new parallel version of this algorithm, which is routinely applied in many application domains, including defence and intelligence, precision agriculture, geology, or forestry. We improve considerably the computational cost of this algorithm, and also improve its detection accuracy by incorporating a new method for calculating the orthogonal projection process in which the algorithm is based using the Gram-Schmidt method. Our proposed strategy reduces the computational cost over the a previous implementation of the same algorithm which uses the pseudoinverse operation to compute the orthogonal projection. Our parallel algorithm is implemented on a multicore cluster system made up of of sixteen nodes, with two CPUs of four cores per node, and quantitatively evaluated using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York and over the Cuprite mining district, Nevada, United States.
In this paper we propose a parallel tabu search algorithm to solve the problem of the distributed database optimal logical structure synthesis. We provide a reader with information about the performance metrics of our...
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ISBN:
(纸本)9783642245107
In this paper we propose a parallel tabu search algorithm to solve the problem of the distributed database optimal logical structure synthesis. We provide a reader with information about the performance metrics of our parallel algorithm and the quality of the solutions obtained in comparison with the earlier developed consecutive algorithm and other methods.
Virtual microscopy systems are typically implemented following standard client-server architectures, under which the server must store a huge quantity of data. The server must attend requests from many clients as seve...
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ISBN:
(纸本)9780819485052
Virtual microscopy systems are typically implemented following standard client-server architectures, under which the server must store a huge quantity of data. The server must attend requests from many clients as several Regions of Interest (RoIs) at any desired levels of magnification and quality. The communication bandwidth limitation, the I/O image data accesses, the decompression processing and specific raw image data operations such as clipping or zooming to a desired magnification, are highly time-consuming processes. All this together may result in poor navigation experiences with annoying effects produced by the delayed response times. This article presents a virtual microscope system with a distributed storage system and parallelprocessing. The system attends each request in parallel, using a clustered java virtual machine and a distributed filesystem. images are stored in JPEG2000 which allows natural parallelization by splitting the image data into a set of small codeblocks that contain independent information of an image patch, namely, a particular magnification, a specific image location and a pre-established quality level. The compressed J2K file is replicated within the distributed Filesystem, providing fault tolerance and fast access. A requested RoI is split into stripes which are independently decoded for the distributed filesystem, using an index file which allows to easily locate the particular node containing the required set of codeblocks. When comparing with a non-parallelized version of the virtual microscope software, user experience is improved by speeding up RoI displaying in about 60 % using two computers.
Lately, the use of GPUs is dominant in the field of high performance computing systems for computer graphics. However, since there is "not good for everything" solution, GPUs have also some drawbacks that ma...
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