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Multi-label classification has attracted much attention in recent years due to various applications in real world. There have been many algorithms to deal with this problem. However, there is no algorithm that simultaneously exploits the locality in the instance space and label space which plays an important role in generating a satisfactory model. In this paper we present such an algorithm. It utilizes...
Virtualization is one of the most promising ways to enhance the scalability of data centers. Individual physical machines(PMs) can be carved into multiple virtual machines(VMs) for consolidation. In fact, most data centers are still using static greedy allocation of resources to VMs, regardless of the resource characteristics of both VMs and PMs. In this paper, we address that by optimizing the placement...
In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a...
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