Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successf...
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ISBN:
(纸本)9781479999897
Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successful image recommendation. The context information is expressed frequently in terms of high-order relations, such as the relations among users, tags, and images. Hypergraphs can model the aforementioned high-order relations between their vertices (i.e., users, user social groups, tags, geo-tags, and images) by hyper-edges, whose influence can be assessed by properly estimating their weights. Here, an efficient adaptive hypergraph weight estimation is proposed for image tagging. In particular, both equality and inequality constraints enforced during hypergraph learning are taken into account and an efficient adaptation step selection using the Armijo rule is proposed. Experiments conducted on a dataset demonstrate the superior performance of the proposed approach compared to the state-of-the-art.
Application of fast adaptive algorithms to the linear receiver structures for coherent demodulation in asynchronous code-division multiple-access (CDMA) systems are considered. The convergence rate of fast algorithms ...
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Application of fast adaptive algorithms to the linear receiver structures for coherent demodulation in asynchronous code-division multiple-access (CDMA) systems are considered. The convergence rate of fast algorithms is insensitive to the eigenvalue spread to the multiple access signal correlation matrix. The eigenvalue spread caused by near-far effect, the number of users close to the spreading gain and presence of strong narrowband interference are investigated.
This paper defines a phase-only, gradient based adaptive algorithm analogous to the Least Mean Square (LMS) algorithm. A phase-only perturbation algorithm is also defined.
This paper defines a phase-only, gradient based adaptive algorithm analogous to the Least Mean Square (LMS) algorithm. A phase-only perturbation algorithm is also defined.
We report on the development of a new class of parallel computation algorithm for low-level scene analysis. The algorithm is a high resolution, high speed estimator for boundary extraction of simple objects imaged und...
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We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. Th...
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In many signal processing applications it is common to preprocess the input data before they are fed into various adaptive algorithms used to separate original signals from a noisy mixture. The preprocessing transform...
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In this paper, different adaptive algorithms for stereophonic acoustic echo cancellation are compared. The algorithms include the simple LMS algorithm and two specialized two-channel adaptive algorithms. Due to the hi...
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adaptive beamforming is one of the core technology of the smart antenna system. Two different adaptive algorithms which adopt the minimum mean square algorithm (LMS) and recursive least squares algorithm (RLS) are emp...
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In CDMA system, the smart antenna system is mainly applied to the BS. The smart antenna can adjust the direction pattern adaptively and reduce the interference signals using some adaptive interference nulling algorith...
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We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. Th...
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We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed to achieve a specified accuracy. The second algorithm is adaptive and parameter-free, albeit not optimal.
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