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TOC Alert for Publication# 5962385 2016June 23<![CDATA[Table of contents]]>277C1C1126<![CDATA[IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information]]>277C2C2168<![CDATA[Probe Machine]]>277140514166156<![CDATA[Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection]]> as a basic subspace, namely, -ball, in the sense that it represents local shape variance under the certain distance metric. Using these -balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.]]>277141714283485<![CDATA[Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery]]>et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain–computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.]]>277142914444295<![CDATA[Alternative Multiview Maximum Entropy Discrimination]]> over the first-view classifier parameter and over the second-view classifier parameter . We name the new MVMED framework as alternative MVMED (AMVMED), which enforces the posteriors of two view margins to be equal. The proposed AMVMED is more flexible than the existing MVMED, because compared with MVMED, which optimizes one relative entropy, AMVMED assigns one relative entropy term to each of the two views, thus incorporating a tradeoff between the two views. We give the detailed solving procedure, which can be divided into two steps. The first step is solving our optimization problem without considering the equal margin posteriors from two views, and then, in the second step, we consider the equal posteriors. Experimental results on multiple real-world data sets verify the effectiveness of the AMVMED, and comparisons with MVMED are also reported.]]>277144514561462<![CDATA[Parallel Online Temporal Difference Learning for Motor Control]]> to , with a real-time learning speed of less than half a minute. The results are competitive with state-of-the-art policy search.]]>277145714682139<![CDATA[Sparse Uncorrelated Linear Discriminant Analysis for Undersampled Problems]]> -norm from all minimum dimension solutions of the generalized ULDA. The problem is then formulated as an -minimization problem with orthogonality constraint. To solve this problem, we devise two algorithms: 1) by applying the linearized alternating direction method of multipliers and 2) by applying the accelerated linearized Bregman method. Simulation studies and high-dimensional real data examples demonstrate that our algorithms not only compute extremely sparse solutions but also perform well in classification.]]>277146914853209<![CDATA[Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity]]>277148615011870<![CDATA[Compound Rank-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> Projections for Bilinear Analysis]]> projection (CRP) algorithm for bilinear analysis. The CRP deals with matrices directly without transforming them into vectors, and it, therefore, preserves the correlations within the matrix and decreases the computation complexity. Different from the existing 2-D discriminant analysis algorithms, objective function values of CRP increase monotonically. In addition, the CRP utilizes multiple rank- projection models to enable a larger search space in which the optimal solution can be found. In this way, the discriminant ability is enhanced. We have tested our approach on five data sets, including UUIm, CVL, Pointing’04, USPS, and Coil20. Experimental results show that the performance of our proposed CRP performs better than other algorithms in terms of classification accuracy.]]>277150215132732<![CDATA[Constrained Clustering With Nonnegative Matrix Factorization]]>277151415265772<![CDATA[Control of Large-Scale Boolean Networks via Network Aggregation]]>27715271536957<![CDATA[Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration]]> , where is the fuzzy state dimensionality and is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experimen-
on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system.]]>277153715492227<![CDATA[Image Super-Resolution via Adaptive <inline-formula> <tex-math notation="LaTeX">$ell _{p} (0<p<1)$ </tex-math></inline-formula> Regularization and Sparse Representation]]> or norm optimization problem. The optimization is a nonconvex and NP-hard problem, while the optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the regularization method. We also consider the best choice for regularization with all in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter adaptively, and we discuss an alternate iteration method for selecting and . We perform experiments, which demonstrates that the proposed regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.]]>277155015613814<![CDATA[Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints]]>277156215711925<![CDATA[Learning Spike Time Codes Through Morphological Learning With Binary Synapses]]>27715721577731<![CDATA[Saturated Finite Interval Iterative Learning for Tracking of Dynamic Systems With HNN-Structural Output]]>27715781584768<![CDATA[Pinning Control Design for the Stabilization of Boolean Networks]]>27715851590187<![CDATA[Can the Virtual Labels Obtained by Traditional LP Approaches Be Well Encoded in WLR?]]>et al. (2011) highlighted two important characteristics of SRW nonexistent in the previous LP approaches: outlier detection and probability value output, which guarantee the elegant encoding of the resultant virtual labels in the weighted label regression. However, in this brief, we show that the relationship between the SRW and the previous work on LP is very close. Naturally, a problem deserving investigation is whether traditional LP approaches are indeed unable to share the above two characteristics of SRW. We aim to address this problem.]]>277159115981261<![CDATA[Call For Papers: IEEE Transactions on Cognitive and Developmental Systems]]>277159915991187<![CDATA[Call For Papers: IEEE World Congress on Computational Intelligence]]>277160016004264<![CDATA[IEEE Computational Intelligence Society Information]]>277C3C3160<![CDATA[IEEE Transactions on Neural Networks information for authors]]>277C4C4189