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In this paper, we study the class imbalance problem in statistical background subtraction. Firstly, we discuss the imbalance essence in background subtraction, and conclude that foreground and background are inherently imbalanced. Secondly, following the imbalanced learning strategy in machine learning, we present a spatio-temporal over-sampling method to resolve the class imbalance in background...
Recent spectrum auction results have shown that the spectrum is usually sold at a very high unit price. Small network providers may not be able to afford it individually. Inspired by the group buying service on the Internet, group buying strategy has been introduced into the design for spectrum auctions to increase the buying power of small network providers as a whole. In this paper, we consider...
Auctions provide a platform for licensed spectrum users to trade their underutilized spectrum with unlicensed users. Existing spectrum auctions either do not apply to the scenarios where multiple sellers and buyers both make offers, or assume the knowledge of the users' valuation distribution for maximizing the profit of the auction. To fill this void, we design PROMISE, a framework for spectrum double...
Canonical correlation analysis (CCA) has been widely used in pattern recognition and machine learning. However, both CCA and its extensions sometimes cannot give satisfactory results. In this paper, we propose a new CCA-type method termed sparse representation based discriminative CCA (SPDCCA) by incorporating sparse representation and discriminative information simultaneously into traditional CCA...
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In...
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