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	1. Approximating Algorithms for Computing Energy Constrained MinimumCost Steiner Trees
	ZOU Nianchen, GUO Longkun, HUANG Peihuang
	Computer Science and Technology 30 May 2015
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	Abstract:Green data transmission is important for wireless networks, such assensor networks, mobile networks, etc. This paper gives approximationalgorithms for constructing energy constrained minimum cost Steinertrees (ECMST), a topology for data multicast considering both savingenergy and minimizing the occupied resource. For a given undirectedgraph, in which each edge is with nonnegative integral cost and energyconsumption, the ECMST problem is to compute a minimum cost tree spanningall specified terminals and satisfying a given energy consumptionconstraint. Apparently ECMST is NP-hard, since it includes the minimumSteiner tree problem which is known NP-hard.The paper first presents an approximation algorithm by extendingByrka et al.'s method via Lagrangian relaxation. Then, the paper improves the ratio of the approximation algorithm to $(2,,3)$by replacing components of the computed Steiner tree. The improvementis mainly based on three ingredients: a generalization of the $k$-Steinerratio against ECMST, the observation that ECMST is pseudo-polynomialsolvable when the number of the terminals are fixed, and the extensionof Byrka and et al.'s approximation algorithm. To the best of ourknowledge, our algorithm is with the best ratio in the current stateof the art. Although Ravi and Goemanshave proposed a $(1+epsilon,,1)$-approximation algorithm for thespecial case when all vertices are terminals, their method can notbe applied to ECMST, since their method is based on a matroid propertywhich doesn't hold for ECMST.
	TO cite this article:ZOU Nianchen, GUO Longkun, HUANG Peihuang. Approximating Algorithms for Computing Energy Constrained MinimumCost Steiner Trees[OL].[30 May 2015] http://en.paper.edu.cn/en_releasepaper/content/4640705


	2. Weighted Extension of OMP for Discriminative Dictionary Learning
	Chen Naheng
	Computer Science and Technology 10 July 2014
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	Abstract:Classification based on sparse representation has received a great deal of attention in recent years. In this paper, a weighted extension of OMP for classification is presented. It can be used in dictionary learning algorithms when dealing with classification issue. The proposed method adds a weighted parameter to the OMP algorithm; therefore discriminative power is enhanced and leads a faster convergence. This method is evaluated in the face recognition on Extended YaleB database and AR database. The result shows that the method achieves comparable recognition accuracy to the state-of-the-art result and runs 18 times faster in Extended YaleB and 30 times faster in AR.
	TO cite this article:Chen Naheng. Weighted Extension of OMP for Discriminative Dictionary Learning[OL].[10 July 2014] http://en.paper.edu.cn/en_releasepaper/content/4601431

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