This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces ...
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This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.
A novel Quantum-behaved Particle Swarm Optimization algorithm with probability(P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original e...
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A novel Quantum-behaved Particle Swarm Optimization algorithm with probability(P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.
It is shown by particle-in-cell simulations that a narrow electron beam with high energy and charge density can be generated in a subcritical-density plasma by two consecutive laser pulses. Although the first laser pu...
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It is shown by particle-in-cell simulations that a narrow electron beam with high energy and charge density can be generated in a subcritical-density plasma by two consecutive laser pulses. Although the first laser pulse dissipates rapidly, the second pulse can propagate for a long distance in the thin wake channel created by the first pulse and can further accelerate the preaccelerated electrons therein. Given that the second pulse also self-focuses, the resulting electron beam has a narrow waist and high charge and energy densities. Such beams are useful for enhancing the target-back space-charge field in target normal sheath acceleration of ions and bremsstrahlung sources, among others.
Nowadays, cloud providers of 'Infrastructure as a service' require datacenter networks to support virtualization and multi-tenancy at large scale, while it brings a grand challenge to datacenters. Traditional ...
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In China, the expressway isn’t free. When a vehicle exits, the exit toll station needs to calculate the toll according to the vehicle trajectory obtained by sending a trajectory query task to the trajectory center re...
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Cloud services must upgrade continuously in order to maintain competitive. However, a large body of empirical evidence suggests that, upgrade procedures used in practice are failure-prone and often cause planned or un...
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Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy...
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Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent (LI) with lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a convenient option to solve tile problem. The dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping (MWDTW) algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model (HMM) significantly, and is also six times faster than DTW overall.
Pervasive software should be able to adapt itself to the changing environments and user requirements. Obviously, it will bring great challenges to the software engineering practice. This paper proposes AUModel, a conc...
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Asynchrony based overlapping of computation and communication is commonly used in MPI applications. However, this overlapping introduces synchronization errors frequently in asynchronous MPI programming. In this paper...
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Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the parti...
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