Design of (4, 8) Binary Code with MDS and Zigzag-Decodable Property

Dai, Mingjun; Lu, Zexin; Shen, Dan; Wang, Hui; Chen, Bin; Lin, Xiaohui; Zhang, Shengli; Zhang, Li; Liu, Hongwei

doi:10.1007/s11277-016-3234-8

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…The performance of MRV2 is improved based on our strategy both on job execution time and resource consumption, no matter in a single-job environment or a multi-job environment compared with other strategies. In the future, we would try to study cloud storage as shown in [27] [28] which seems to be applied to optimize the mechanism of HDFS.…”

Section: Resultsmentioning

confidence: 99%

An Adaptively Speculative Execution Strategy Based on Real-Time Resource Awareness in a Multi-Job Heterogeneous Environment

2017

KSII TIIS

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MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

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Section: Resultsmentioning

confidence: 99%

An Adaptively Speculative Execution Strategy Based on Real-Time Resource Awareness in a Multi-Job Heterogeneous Environment

2017

KSII TIIS

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“…The idea of classical MDS (cMDS) is to embed the given objects into a low dimensional space based on a Euclidean distance matrix. Recently, there has been great progress in MDS, such as the semismooth Newton method for nearest Euclidean distance matrix problem (Qi (2013); Qi and Yuan (2014)), the inexact smoothing Newton method for nonmetric MDS (Li and Qi (2017)), as well as the applications of MDS in nonlinear dimension reduction Qi (2016, 2017)), binary code learning (Dai et al (2016)), and sensor Ordinal Distance Metric Learning with MDS network localization (Qi et al (2013)).…”

Section: Introductionmentioning

confidence: 99%

Ordinal Distance Metric Learning with MDS for Image Ranking

2018

Asia Pac. J. Oper. Res.

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Image ranking is to rank images based on some known ranked images. In this paper, we propose an improved linear ordinal distance metric learning approach based on the linear distance metric learning model in Li et al. (2015). By decomposing the distance metric A as L T L, the problem can be cast as looking for a linear map between two sets of points in different spaces, meanwhile maintaining some data structures. The ordinal relation of the labels can be maintained via classical multidimensional scaling, a popular tool for dimension reduction in statistics. A least squares fitting term is then introduced to the cost function, which can also maintain the local data structure. The resulting model is an unconstrained problem, and can better fit the data structure. Extensive numerical results demonstrate the improvement of the new approach over the linear distance metric learning model both in speed and ranking performance.Ordinal Distance Metric Learning with MDS IntroductionGiven a labeled image dataset (referred as the training set), image ranking is to find the most relevant images for a query image based on the training set. Different from binary classification and multiclassification, the labels of the training set in image ranking often have an order, for example, age. The two important and challenging aims for image ranking are as follows. The first aim is to find which class the query image belongs to, and the second is to find the most relevant images in the specific class. The first aim actually falls into ordinal regression in statistics, where different approaches have been proposed, see Gutierrez et al. (2016) for a survey on ordinal regression and Qiao (2015), Wang et al. (2017) for the recent development. However, the second aim makes image ranking different from ordinal regression since the training images having the same label with query image need to be further ranked. Therefore, a direct extension of methods for ordinal regression is not appropriate for image ranking.As for the second aim, to find the most relevant images, a natural way is to use Euclidean distance between images to measure their dissimilarities. However, as we will show later, in most cases, Euclidean distance is not appropriate for dissimilarity. A practical way is to learn a distance metric (denoted as A) to measure the distances between images. This is referred as distance metric learning (DML). Then for a query image, the most relevant images are those with smallest distances under metric A. Many DML methods have been developed for image classification and clustering tasks. For example, the SDP approach proposed by Xing et al. (2003), an online learning algorithm proposed by Shalev-Shwartz et al. (2004), a neighborhood component analysis (NCA) by Goldberger et al. (2004), and so on (Bar-hillel et al. (2003); Shen et al. (2010); Yang et al. ( 2007)). However, most of these methods didn't assume the labels are ordered. Therefore, they can not be directly used for image ranking.Recently, Li et al. (2015) firstly introduced ordin...

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