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As a new computing paradigm, cloud computing has significantly contributed to the rapid development of massive data centers. However, the corresponding energy issue becomes increasingly challenging. In this paper, we focus on the energy saving issue for virtual machine (VM) selections on an overloaded host in a cloud computing environment. We analyze the energy influencing factors during a VM migration,...
This paper addresses the problem of learning meaningful human action attributes from high-dimensional video sequences based on union-of-subspaces (UoS) model. The model hypothesizes that each action attribute is represented by a subspace. It puts forth an extension of existing low-rank representation (LRR), termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for...
A hierarchical union-of-subspaces model is proposed for performing semi-supervised human activity summarization in large streams of video data. The union of low-dimensional subspaces model is used to learn meaningful action attributes from a collection of high-dimensional video sequences of human activities. An approach called hierarchical sparse subspace clustering (HSSC) is developed to learn this...
This paper addresses the problem of learning a collection of nonlinear manifolds. Inspired by kernel methods, it puts forth a generalization of the kernel subspace model, termed the Metric-Constrained Kernel Union-of-Subspaces (MC-KUoS) model. It then develops an iterative method for learning of an MC-KUoS whose solution is based on the data representation capability of the manifolds and distances...
The linguistic weighted average (LWA) of interval type-2 fuzzy sets is an extension of the fuzzy weighted average (FWA) of type-1 fuzzy sets. Currently, the commonly used methods of both FWA and LWA are based on a-cuts decomposition of the type-1 of type-2 fuzzy sets, which involves large amount of calculations, and the result is not accurate. In this paper, we propose a new algorithm to obtain the...
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