基于流形排序和联合连通性先验的显著性目标检测

为了进一步提高显著性目标检测的准确性,提出基于不同特征流形排序和联合连通性先验的显著性检测算法.针对现有基于流形排序的算法在图的构建中存在的边权重计算和顶点的连接问题,使用不同种特征计算顶点间边的权重,并且改进顶点的连接方式,得到流形排序显著图.同时结合边界连通性先验和前景连通性先验得到联合连通性先验显著图.在不同尺度下进一步融合两种显著性结果,得到最终的显著图.通过与16种算法在4种数据集上的对比表明,文中算法可以得到更清晰、准确的检测结果.     

层次图融合的显著性检测

显著性目标检测在物体分割、自适应压缩和物体识别等领域有众多应用,从自然场景中准确检测出最重要的区域一直是个挑战。针对现有的基于图的流形排序算法,因忽略特征的空间信息而导致检测准确率不高的问题,提出了一种基于流形排序的多尺度显著性检测算法。首先对原始图像进行多尺度下的超像素分解。然后利用边界先验,根据流形排序算法计算查询点与其余结点的相关度排序。最后通过构建图模型,从多层结构中分析显著性线索,对显著图进行融合得到最终结果。在ASD、CSSD、ECSSD和SOD数据集上,同9种流形算法进行对比实验,结果表明该算法在保持高查全率的同时也提高了准确率。     

基于自然邻居流形排序图像检索技术研究

流形排序算法被广泛地应用到半监督学习领域中,然而其性能紧紧依赖于底层图结构。首先,本文提出了一种全新的图结构—自然邻居图,并阐述了其构造方法。然后,将这种图结构应用到基于流形排序的图像检索框架下,同基于k-NN的图像检索算法相比,最大优势是此算法不需要提前指定参数k。最后,实验证明了基于本文提出的自然邻居图的流形排序算法的效果优于基于k-NN图的流形排序算法。     

基于卷积神经网络和流形排序的图像检索算法

针对基于内容的图像检索(CBIR)中低层视觉特征与用户对图像理解的高层语义不一致以及传统的距离度量方式难以真实反映图像之间相似程度等问题,提出了一种基于卷积神经网络(CNN)和流形排序的图像检索算法。首先,将图像输入CNN,通过多层神经网络对图像的监督学习,提取网络中全连接层的图像特征;其次,对图像特征进行归一化处理,然后用高效流形排序(EMR)算法对查询图像所返回的结果进行排序;最后,根据排序的结果返回最相似的图像。在corel数据集上,深度图像特征比基于场景描述的图像特征的平均查准率(mAP)提高了53.74%,流形排序比余弦距离度量方式的mAP提高了18.34%。实验结果表明,所提算法能够有效地提高图像检索的准确率。     

基于自适应显著特征选择的动态加权平均行人识别模型

在跨场景行人识别过程中,为了解决多种特征以一个固定的权重融合导致行人识别率低、识别速度慢的问题,提出基于自适应特征选择的动态加权平均排名行人识别方法。首先,将GrabCut算法和基于流形排序显著性检测算法相融合,提高行人外观特征提取的准确性;然后,提出自适应显著特征选择方法,有效地提取行人特征描述;最后,通过动态加权平均排名模型将多特征融合。实验表明,所提出的方法提高了行人识别的准确性,同时对姿态的变化具有较好的鲁棒性。     

基于层次隐马尔可夫模型和神经网络的个性化推荐算法

传统推荐系统将推荐准确性作为主要目标,而推荐结果的多样性和个性化有所欠缺。对此,设计一种基于层次隐马尔可夫模型和神经网络的推荐算法。采用层次隐马尔可夫模型建模用户喜好和上下文环境的关系,并通过隐马尔可夫模型预测上下文。设计神经网络结构来解决协同过滤推荐的问题,同时神经网络满足贝叶斯个性化排序的条件,实现对推荐列表的个性化排序。实验结果表明,该算法在保持推荐准确性的前提下,提高了推荐的多样性和个性化。     

面向个性化推荐系统的二分网络协同过滤算法研究

为提高个性化推荐系统的推荐效率和准确性, 提出了个性化推荐系统的二分网络协同过滤算法。协同过滤算法引入二分网络描述个性化推荐系统, 使用灰色关联度来度量用户相似性和项目相似性, 对灰色关联相似度加权求和预测用户对项目的预测打分值, 从而提供给用户排序后的项目列表。实验结果表明, 协同过滤算法有效提高了过滤推荐的精准度和可靠性, 具有良好的推荐效果。     

基于Manifold Ranking和结合前景背景特征的显著性检测

针对基于图和流形排序（Manifold Ranking）的显著性检测算法（MR算法）过度依赖边界节点的背景特征的问题,提出一种改进的结合前景背景特征的显著性检测算法。首先,对图像进行超像素分割,建立闭环图模型;然后利用流形排序算法根据图像前景特征和背景特征分别得出前景种子和背景种子;再通过亮度和颜色特征对两类种子进行结合,筛选出更为准确的查询节点;最后再利用流形排序算法进行显著值计算,得到最终的显著图。实验表明,改进方法与MR算法相比在精确率、召回率、F值等多个评价指标上均有明显提升,得到的显著图更接近真值。     

基于Pairwise排序学习的因子分解推荐算法

针对基于内存的协同过滤推荐算法存在推荐列表排序效果不佳的问题,提出基于Pairwise排序学习的因子分解推荐算法(简称Pairwise-SVD推荐算法)。新算法将因子分解的预测结果作为排序学习算法的输入,把排序问题转化成分类问题使用排序学习理论进行排序产生推荐列表。实验结果表明相比基于内存的协同过滤推荐算法,Pairwise-SVD推荐算法的排序效果更佳。其在指标Kendall-tau上提高了近一倍,在指标MRR上提高了近30%,且在指标MAP上也有小幅提高。     

成对约束降维集成下的MicroRNA预测

MicroRNA(miRNA)是一类在生物体内发挥重要调控作用的非编码小RNA,对miRNA的预测有助于研究和理解其生物学功能。已经提出的基于成对约束的降维算法(local semi-supervised linear discriminant analysis,LSLDA)在对miRNA降维的同时,也能保持数据的局部结构信息和判别能力,可有效改进miRNA的预测性能。因此,在LSLDA算法基础上,提出了一种新的集成LSLDA算法(ensemble of local semi-supervised linear discriminant analysis,En-LSLDA)。该算法对不同约束个数下的分类结果进行集成,以集成结果作为最后的分类结果,以此进一步改进miRNA的预测性能。miRNA数据集上的实验结果表明,En-LSLDA算法是有效可行的。同时,UCI数据集上的实验结果也验证了新提出的集成方法同样适用于其他数据集。     

结合流形排序和区域匹配的图像检索

给出一种基于数据流形排序(Manifold Ranking)和分割区域匹配的图像检索方法.在Manifold Ranking方法的基础上,提出区域匹配图(Region Matching Graph,RMG)的方法,通过计算图像的区域匹配权值,进行第二次相似性匹配,提高了匹配准确性.在Corel图像数据库对该方法进行了检索仿真,结果表明该方法能有效提高检索的准确性.     

Manifold Ranking using Hessian Energy

In recent years, learning on manifolds has attracted much attention in the academia community. The idea that the distribution of real-life data forms a low dimensional manifold embedded in the ambient space works quite well in practice, with applications such as ranking, dimensionality reduction, semi-supervised learning and clustering. This paper focuses on ranking on manifolds. Traditional manifold ranking methods try to learn a ranking function that varies smoothly along the data manifold by using a Laplacian regularizer. However, the Laplacian regularization suffers from the issue that the solution is biased towards constant functions. In this work, we propose using second-order Hessian energy as regularization for manifold ranking. Hessian energy overcomes the above issue by only penalizing accelerated variation of the ranking function along the geodesics of the data manifold. We also develop a manifold ranking framework for general graphs/hypergraphs for which we do not have an original feature space (i.e. the ambient space). We evaluate our ranking method on the COREL image dataset and a rich media dataset crawled from Last.fm. The experimental results indicate that our manifold ranking method is effective and outperforms traditional graph Laplacian based ranking method.     

基于协同过滤的连续黑箱优化问题元启发算法选择

算法选择(AS)问题旨在为给定问题在算法集合中选择最佳算法.随着优化算法的不断提出,算法选择问题是优化领域亟待解决的问题.提出基于聚类的元启发算法五星评价体系,将算法性能指标映射至整数评价以减小评价空间.通过测试24种常见优化算法与4种最新CEC大赛优胜算法在219种、3000多个标准测试问题上的性能,得到评价矩阵.将评价矩阵作为训练数据,使用协同过滤(CF)算法建立算法评价的预测模型.使用该模型预测算法集内的所有算法在新问题上的评价,结果显示所提出方法预测精度较高,超过90%的预测最佳算法为最终可行算法.敏感性分析显示,该方法在先验信息有限的情况下仍可以保持较高的预测精度.     

大数据层面的 microRNA 功能相似性分析

随着大数据时代的来临,microRNA与基因的序列数据不断增加,如何从大量的数据中挖掘有生物学意义的信息成为新的热点问题。研究表明microRNA间以协作的方式在疾病中发挥作用,并呈现出网络的结构化趋势。因此,系统分析不同microRNA间的相似性将在疾病生物学标记挖掘等研究领域起到关键的桥梁作用。而microRNA通过调节其靶基因发挥作用,所以本研究将充分利用现有靶基因数据,从功能角度分析microRNA间的相似性。研究选取前期工作所得的靶基因优化列表,利用富集分析将基因集合转化为功能节点集合,并在此基础上利用集合相似性测度计算microRNA对在不同层面的功能一致性。结果表明,相同家族的microRNA倾向于调控相同或相似的靶基因;类比于非靶基因,microRNA靶基因倾向于共享较多相似的细胞组分,而在生物学通路及生物学过程中则具有相对较低的相似性。     

Editorial of the Special Issue on Manifold Learning

In many information analysis tasks, one is often confronted with thousands to millions dimensional data, such as images, documents, videos, web data, bioinformatics data, etc. Conventional statistical and computational tools are severely inadequate for processing and analysing high-dimensional data due to the curse of dimensionality, where we often need to conduct inference with a limited number of samples. On the other hand, naturally occurring data may be generated by structured systems with possibly much fewer degrees of freedom than the ambient dimension would suggest. Recently, various works have considered the case when the data is sampled from a submanifold embedded in the much higher dimensional Euclidean space. Learning with full consideration of the low dimensional manifold structure, or specifically the intrinsic topological and geometrical properties of the data manifold is referred to as manifold learning, which has been receiving growing attention in our community in recent years. This special issue is to attract articles that (a) address the frontier problems in the scientific principles of manifold learning, and (b) report empirical studies and applications of manifold learning algorithms, including but not limited to pattern recognition, computer vision, web mining, image processing and so on. A total of 13 submissions were received. The papers included in this special issue are selected based on the reviews by experts in the subject area according to the journal''s procedure and quality standard. Each paper is reviewed by at least two reviewers and some of the papers were revised for two rounds according to the reviewers'' comments. The special issue includes 6 papers in total: 3 papers on the foundational theories of manifold learning, 2 papers on graph-based methods, and 1 paper on the application of manifold learning to video compression. The papers on the foundational theories of manifold learning cover the topics about the generalization ability of manifold learning, manifold ranking, and multi-manifold factorization. In the paper entitled ``Manifold Learning: Generalizing Ability and Tangential Proximity'', Bernstein and Kuleshov propose a tangential proximity based technique to address the generalized manifold learning problem. The proposed method ensures not only proximity between the points and their reconstructed values but also proximity between the corresponding tangent spaces. The traditional manifold ranking methods are based on the Laplacian regularization, which suffers from the issue that the solution is biased towards constant functions. To overcome this issue, in the paper entitled ``Manifold Ranking using Hessian Energy'', Guan et al. propose to use the second-order Hessian energy as regularization for manifold ranking. In the paper entitled ``Multi-Manifold Concept Factorization for Data Clustering'', Li et al. incorporate the multi-manifold ensemble learning into concept factorization to better preserve the local structure of the data, thus yielding more satisfactory clustering results. The papers on graph-based methods cover the topics about label propagation and graph-based dimensionality reduction. In the paper entitled ``Bidirectional Label Propagation over Graphs'', Liu et al. propose a novel label propagation algorithm to propagate labels along positive and negative edges in the graph. The construction of the graph is novel against the conventional approach by incorporating the dissimilarity among data points into the affinity matrix. In the paper entitled ``Locally Regressive Projections'', Lijun Zhang proposes a novel graph-based dimensionality reduction method that captures the local discriminative structure of the data space. The key idea is to fit a linear model locally around each data point, and then use the fitting error to measure the performance of dimensionality reduction. In the last paper entitled ``Combining Active and Semi-Supervised Learning for Video Compression'', motivated from manifold regularization, Zhang and Ji propose a machine learning approach for video compression. Active learning is used to select the most representative pixels in the encoding process, and semi-supervised learning is used to recover the color video in the decoding process. One remarking property of this approach is that the active learning algorithm shares the same loss function as the semi-supervised learning algorithm, providing a unified framework for video compression. Many people have been involved in making this special issue possible. The guest editor would like to express his gratitude to all the contributing authors for their insightful work on manifold learning. The guest editor would like to thank the reviewers for their comments and useful suggestions in order to improve the quality of the papers. The guest editor would also like to thank Prof. Ruqian Lu, the editor-in-chief of the International Journal of Software and Informatics, for providing the precious opportunity to publish this special issue. Finally, we hope the reader will enjoy this special issue and find it useful.     

基于Nyström 低阶近似的半监督流形排序图像检索

实际图像检索过程中,用户提供的相关反馈有限,但存在大量未标记图像数据. 本文在前期半监督流形图像检索工作的基础上,提出一种基于Nystrm低阶 近似的半监督流形排序图像检索方法.通过采用半监督的流形正则化框架, 将图像数据嵌入到低维流形结构中进行分类排序,以充分利用大量未标记数据, 并兼顾分类误差、数据分布的几何结构以及分类函数的复杂性.针对半监督学习速度缓慢的问题, 基于Nystrm低阶近似对学习过程进行加速.在较大规模的Corel图像数据集上进行了检索实验, 实验结果表明该方法能获得较好的效果.     

基于多图流形排序的图像显著性检测

针对现有图像显著性检测算法中显著目标检测不完整和显著目标内部不均匀的问题,本文提出了一种基于多图流形排序的图像显著性检测算法.该算法以超像素为节点构造KNN图（K nearest neighbor graph）模型和K正则图（K regular graph）模型,分别在两种图模型上利用流形排序算法计算超像素节点的显著性值,并将每个图模型中超像素节点的显著值加权融合得到最终的显著图.在公开的MSRA-10K、SED2和ECSSD三个数据集上,将本文提出的算法与当前流行的14种算法进行对比,实验结果显示本文算法能够完整地检测出显著目标,并且显著目标内部均匀光滑.     

一种基于流行排序的区域图像检索方法*

提出了一种结合多示例学习和流行排序的图像检索方法,将图像检索作为多示例学习框架下的流行排序,通过给出适合图像在包空间的有效度量方式,将流行排序的方法和多示例学习有效结合起来,从而获得更准确的检索结果。实验结果表明,运用流行排序的区域图像检索方法是可行的,同时,检索结果与传统的区域图像检索方法相比,检索率得到了明显的提高。