Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

A note on local well-posedness of generalized KdV type equations with dissipative perturbations

In this note we report local well-posedness results for the Cauchy problems associated to generalized KdV type equations with dissipative perturbation for given data in the low regularity $L^2$-based Sobolev spaces. The method of proof is based on the {\em contraction mapping principle} employed in some appropriate time weighted spaces.Comment: 14 page

Quantum model for magnetic multivalued recording in coupled multilayers

In this paper, we discuss the possibilities of realizing the magnetic multi-valued (MMV) recording in a magnetic coupled multilayer. The hysteresis loop of a double-layer system is studied analytically, and the conditions for achieving the MMV recording are given. The conditions are studied from different respects, and the phase diagrams for the anisotropic parameters are given in the end.Comment: 8 pages, LaTex formatted, 7 figures (those who are interested please contact the authors requring the figures) Submitted to Physal Review B. Email: [email protected]

Large magnetothermal conductivity of HoMnO_3 single crystals and its relation to the magnetic-field induced transitions of magnetic structure

We study the low-temperature heat transport of HoMnO_3 single crystals to probe the magnetic structures and their transitions induced by magnetic field. It is found that the low-T thermal conductivity (\kappa) shows very strong magnetic-field dependence, with the strongest suppression of nearly 90% and the biggest increase of 20 times of \kappa compared to its zero-field value. In particular, some ``dip"-like features show up in \kappa(H) isotherms for field along both the ab plane and the c axis. These behaviors are found to shed new light on the complex H-T phase diagram and the field-induced re-orientations of Mn^{3+} and Ho^{3+} spin structures. The results also demonstrate a significant spin-phonon coupling in this multiferroic compound.Comment: 5 pages, 4 figures, accepted for publication in Phys. Rev.

Unbalanced Expander Based Compressive Data Gathering in Clustered Wireless Sensor Networks

© 2013 IEEE. CConventional compressive sensing-based data gathering (CS-DG) algorithms require a large number of sensors for each compressive sensing measurement, thereby resulting in high energy consumption in clustered wireless sensor networks (WSNs). To solve this problem, we propose a novel energy-efficient CS-DG algorithm, which exploits the better reconstruction accuracy of the adjacency matrix of an unbalanced expander graph. In the proposed CS-DG algorithm, each measurement is the sum of a few sensory data, which are jointly determined by random sampling and random walks. Through theoretical analysis, we prove that the constructedM×N sparse binary sensing matrix is the adjacency matrix of a (k; ") unbalanced expander graph whenM=D O(N=k) and t=D O.Nc=(kq) for WSNs with Nc clusters, where 0 ≤q≤1 and Nc > k. Simulation results show our proposed CS-DG has better performance than existing algorithms in terms of reconstruction accuracy and energy consumption. When hybrid energy-efficient distributed clustering algorithm is used, to achieve the same reconstruction accuracy, our proposed CS-DG can save energy by at least 27:8%