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Many real-world problems involve multi-view high-dimension-small-sample-size data analysis, such as multi-omics data. The combination of multi-view databases is supposed to provide a better biological significance. However, the multi-view data always contain noise and outlying entries that result in inaccurate and unreliable. It has become an urgent need how to effectively analyze these data. We proposed...
Foreground detection plays a fundamental role in video analysis. Frames with only background information are usually beneficial for many foreground detection algorithms, especially for regression-based methods where the background is recovered from a background basis matrix. However, many regression-based methods ignore the basis selection process or select bases by simple sampling, which may limit...
As traditional wireless cellular networks are facing a rapid growth of traffic demand, future networks may consist of a large number of small cells which are densely deployed. However, networks in dense environment is exposed to strong inter-cell interference problem which is critical to networks capacity. To solve the problem, we use the Graph Coloring Algorithm (GCA) to divide the small cells into...
As traditional wireless cellular networks are facing a rapid growth of traffic demand, the future networks may consist of a large number of small cells which are densely deployed. However, a large number of small cell Base Stations (BSs) will consume a lot of energy, which is not conductive to improve the network Energy Efficiency (EE). The purpose of our research is to optimize the dense small cell...
Feature selection only using wrapper method in high-dimensional data space is always time-consuming. A new feature selection method, named fast static particle swarm optimization, is proposed for tackling this problem. It treats the whole initial feature set as a static particle swarm in which no new particle would be generated in high dimensional space, and the proposed method takes filter and wrapper...
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