Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

移动机器人数学模型近似线性化及反馈镇定

研究了非线性控制理论中的近似线性化方法在移动机器人控制上的应用问题。针对机器人控制领域中多输入多输出(MIMO)仿射非线性系统,研究了一种基于平衡流形的近似线性化算法,并用此算法解决了一类完整约束正交轮式全方位移动机器人(WMR)的镇定问题。仿真分析表明,此方法不仅能够实现系统的镇定,而且降低了因平衡工作点变动给系统稳定性带来的影响,同时也大大地简化了对非线性系统的综合设计过程,具有良好的控制效果和实用性。

奇异摄动控制系统:理论与应用

系统地回顾了近年来奇异摄动控制技术的发展 ,主要包括线性奇异摄动系统的稳定性分析与镇定、最优控制、H∞ 控制 ,非线性奇异摄动系统的镇定、优化控制和基于积分流形的几何方法 ,以及奇异摄动技术在实际工业 ,例如机器人领域、航天技术领域和工程工业、制造业等中的成功应用 .并指出了这一领域进一步研究的方向

二维流形三角网格模型顶点法矢量估计

提出一种以顶点的一邻域中三角形在该顶点处的顶角与对应三角形的面积比值加权三角面法矢量估计二维流形三角网格模型顶点法矢量的方法.回顾了现有的五种顶点法矢量估计方法,然后给出了新的方法.设计了利用理论法矢量与估计法矢量的夹角作为误差评价标准的实验,应用球体和椭球体模型分析了所涉及的6种估计方法的性能。