Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

一种梯度交替迭代设计测量矩阵的方法

在压缩采样中,测量矩阵应该和表达字典有尽可能小的相干性,随机测量矩阵一直被使用是因为其和任何表达字典都有较小的相干性。提出一种基于梯度迭代最小化方法,作为格拉斯曼框架设计的一种变体,通过优化一个初始的随机测量矩阵来得到相干性更小的测量矩阵。仿真结果表明所设计的测量矩阵具有更好的性能。     

基于slope one算法改进评分矩阵填充的协同过滤算法研究

为解决协同过滤算法中的数据稀疏性问题,提出了一种改进的协同过滤算法。该算法使用slope one算法计算出来的评分预测值来填充评分矩阵中的未评分项目,然后在填充后的用户-项目评分矩阵上通过基于用户的协同过滤方法给出推荐。利用slope one算法计算出来的评分预测值作为回填值,既能降低评分矩阵的稀疏性,也保证了回填值的多样性,从而减少均值、中值等单一填充值造成的推荐误差。在MovieLens-1M数据集上对本文改进算法和协同过滤算法及均值中心化处理的算法作五折交叉实验,结果表明,基于评分预测值填充数据后的协同过滤算法有效的缓解了数据稀疏性问题,并且有更好的推荐效果。     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.     

基于流形学习的整体正交稀疏保留鉴别分析

稀疏保留投影是一种有效的特征提取方法,但是其主要关注样本间的全局稀疏重构关系,并且得到的投影变换通常不是正交的。在实际应用中,图像数据往往处于高维空间中的一种低维流形中,正交性一直被认为有利于提高鉴别能力。文中以有监督学习的方式在稀疏保留投影中引入了流形结构保留,并使得投影空间正交,从而提出了一种新的特征提取方法,即基于流形学习的整体正交稀疏保留鉴别分析（MLHOSDA）。在人脸和掌纹图像数据库的实验结果表明此方法具有较好的识别效果。     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

基于压缩采样匹配追踪的稀疏度和稀疏信道联合估计

原始的压缩采样匹配追踪算法依赖于已知稀疏度,因此本文研究了一种稀疏度和稀疏信道联合估计算法。首先提出了一种新的稀疏向量的替代,能够在有限长度的训练序列下,达到较好的稀疏度和信道估计效果。然后通过对稀疏信道估计中的噪声分量的分析,提出了一种稀疏度估计算法,结合信道估计最终给出了一种稀疏度和稀疏信道联合估计算法。仿真结果表明：新的稀疏向量的替代在稀疏度和信道估计方面都有明显的优势,并且提出的稀疏度和稀疏信道联合估计算法在性能上好于mCoSaMP算法。     

正则化稀疏模型

正则化稀疏模型在机器学习和图像处理等领域发挥着越来越重要的作用,它具有变量选择功能,可以解决建模中的过拟合等问题．Tibshirani 提出的 Lasso 使得正则化稀疏模型真正开始流行．文中总结了各种正则化稀疏模型,指出了各个稀疏模型被提出的原因、所具有的优点、适宜解决的问题及其模型的具体形式．最后,文中还指出了正则化稀疏模型未来的研究方向．     

基于MapReduce与相关子空间的局部离群数据挖掘算法

针对高维海量数据,在MapReduce编程模型下,提出了一种基于相关子空间的局部离群数据挖掘算法.该算法首先利用属性维上的局部稀疏程度,重新定义了相关子空间,从而能够有效地刻画各种局部数据集上的分布特征;其次,利用局部数据集的概率密度,给出了相关子空间中的局部离群因子计算公式,有效地体现了相关子空间中数据对象不服从局部数据集分布特征的程度,并选取离群程度最大的N个数据对象定义为局部离群数据;在此基础上,采用LSH分布式策略,提出了一种MapReduce编程模型下的局部离群数据挖掘算法;最后,采用人工数据集和恒星光谱数据集,实验验证了该算法的有效性、可扩展性和可伸缩性.