一种基于多视图数据的半监督特征选择和聚类算法

高维数据中许多特征之间互不相关或冗余,这给传统的学习算法带来了巨大的挑战。为了解决该问题,特征选择应运而生。与此同时,许多实际问题中数据存在多个视图而且数据的标签难以获取,多视图学习和半监督学习成为机器学习中的热点问题。本文研究怎样从"部分标签"的多视图数据中选择最大相关最小冗余的特征子集,提出一种基于多视图的半监督特征选择方法。为了剔除冗余和无关的特征,探索蕴含于多视图数据中的互补信息以及每个视图中不同特征之间的冗余关系,并利用少量标签数据蕴含的信息协同未标签数据同时进行特征选择。实验结果验证了本算法能够获得很好的特征选择效果及聚类效果。     

Stability-based PAC-Bayes analysis for multi-view learning algorithms

Multi-view learning exploits structural constraints among multiple views to effectively learn from data. Although it has made great methodological achievements in recent years, the current generalization theory is still insufficient to prove the merit of multi-view learning. This paper blends stability into multi-view PAC-Bayes analysis to explore the generalization performance and effectiveness of multi-view learning algorithms. We propose a novel view-consistency regularization to produce an informative prior that helps to obtain a stability-based multi-view bound. Furthermore, we derive an upper bound on the stability coefficient that is involved in the PAC-Bayes bound of multi-view regularization algorithms for the purpose of computation, taking the multi-view support vector machine as an example. Experiments provide strong evidence on the advantageous generalization bounds of multi-view learning over single-view learning. We also explore strengths and weaknesses of the proposed stability-based bound compared with previous non-stability multi-view bounds experimentally.     

A Method for Metric Learning with Multiple-Kernel Embedding

Distance metric learning is rather important for measuring the similarity (/dissimilarity) of two instances in many pattern recognition algorithms. Although many linear Mahalanobis metric learning methods can be extended to their kernelized versions for dealing with the nonlinear structure data, choosing the proper kernel and determining the kernel parameters are still tough problems. Furthermore, the single kernel embedded metric is not suited for the problems with multi-view feature representations. In this paper, we address the problem of metric learning with multiple kernels embedding. By analyzing the existing formulations of metric learning with multiple-kernel embedding, we propose a new framework to learn multi-metrics as well as the corresponding weights jointly, the objective function can be shown to be convex and it can be converted to be a multiple kernel learning-support vector machine problem, which can be solved by existing methods. The experiments on single-view and multi-view data show the effectiveness of our method.     

Feature selection with multi-view data: A survey

This survey aims at providing a state-of-the-art overview of feature selection and fusion strategies, which select and combine multi-view features effectively to accomplish associated tasks. The existing literatures on feature selection approaches are classified into three categories including filter method, wrapper method, and embedded method. Based on the feature selection methods mentioned above, feature-level fusion or known as low-level fusion methodology is further investigated from the perspective of the basic concept, procedure, and applications in analysis tasks as presented in the literatures. Moreover, several distinctive issues that influence the information fusion process such as the use of correlation, confidence level, synchronization, and the optimal features are also emphasized. Finally, we present the adaptive multi-view issues for further research in the area of feature selection and fusion by learning view-specific weights to each view data automatically.     

多视图融合TextRCNN的论文自动推荐算法

传统论文自动推荐算法仅从单视图角度实现分类,缺乏特征融合及多视图语义知识,上下文信息和长距离依赖利用不明显,较难挖掘到深层次文本特征,从而限制学术论文推荐的准确度。针对这些问题,提出了一种基于多视图融合TextRCNN的论文自动推荐模型,该模型融合论文标题、关键词和摘要三个视图特征,利用卷积神经网络(CNN)、双向长短时记忆网络(BiLSTM)和注意力机制构建模型,实现对不同学科方向论文的自动分类及推荐。实验结果表明,设计的论文推荐模型在精确率、召回率和F₁值上均有所提升,比机器学习方法平均提高3.40%、3.57%和3.49%,也优于单视图和已有经典的深度学习方法。该方法有效利用多视图知识和上下文语义信息,提高论文推荐的准确率,进而节约科研工作者检索所需论文所花费时间和精力,进一步提高科研人员的效率,推荐符合其研究需求的学术论文,具有良好的学术价值和应用扩展。     

Semi-supervised multi-view maximum entropy discrimination with expectation Laplacian regularization

Semi-supervised multi-view learning has attracted considerable attention and achieved great success in the machine learning field. This paper proposes a semi-supervised multi-view maximum entropy discrimination approach (SMVMED) with expectation Laplacian regularization for data classification. It takes advantage of the geometric information of the marginal distribution embedded in unlabeled data to construct a semi-supervised classifier. Different from existing methods using Laplacian regularization, we propose to use expectation Laplacian regularization for semi-supervised learning in probabilistic models. We give two implementations of SMVMED and provide their kernel variants. One of them can be relaxed and formulated as a quadratic programming problem that is solved easily. Therefore, for this implementation, we provided two versions which are approximate and exact ones. The experiments on one synthetic and multiple real-world data sets show that SMVMED demonstrates superior performance over semi-supervised single-view maximum entropy discrimination, MVMED and other state-of-the-art semi-supervised multi-view learning methods.     

Multi-view k-proximal plane clustering

Multi-view clustering is an active direction in machine learning and pattern recognition which aims at exploring the consensus and complementary information among multiple views. In the last few years, a number of methods based on multi-view learning have been widely investigated and achieved promising performance. Generally, classical multi-view clustering methods such as multi-view kernel k-means clustering are point-based methods. The performance of point-based methods will be fairly good when the data points are distributed around the center point. The plane-based clustering methods can handle data points that are clustered along a straight line and have never been investigated in multi-view learning. In this paper, we propose a novel multi-view k-proximal plane clustering method, which initializes cluster labels by multi-view spectralclustering and updates whole multi-view cluster hyperplanes and labels alternately until some stopping conditions are satisfied. Extensive experimental results on several benchmark datasets show that the proposed model outperforms other state-of-the-art multi-view algorithms.

     

面向自闭症辅助诊断的无监督模糊特征学习新方法

自闭症患者的行为和认知缺陷与潜在的脑功能异常有关。对于静息态功能磁振图像（functional magnetic resonance imaging, fMRI）高维特征,传统的线性特征提取方法不能充分提取其中的有效信息用于分类。为此,本文面向fMRI数据提出一种新型的无监督模糊特征映射方法,并将其与多视角支持向量机相结合,构建分类模型应用于自闭症的计算机辅助诊断。该方法首先采用多输出TSK模糊系统的规则前件学习方法,将原始特征数据映射到线性可分的高维空间;然后引入流形正则化学习框架,提出新型的无监督模糊特征学习方法,从而得到原输出特征向量的非线性低维嵌入表示;最后使用多视角SVM算法进行分类。实验结果表明:本文方法能够有效提取静息态fMRI数据中的重要特征,在保证模型具有优越且稳定的分类性能的前提下,还可以提高模型的可解释性。     

A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples

Correlated information between multiple views can provide useful information for building robust classifiers. One way to extract correlated features from different views is using canonical correlation analysis (CCA). However, CCA is an unsupervised method and can not preserve discriminant information in feature extraction. In this paper, we first incorporate discriminant information into CCA by using random cross-view correlations between within-class examples. Because of the random property, we can construct a lot of feature extractors based on CCA and random correlation. So furthermore, we fuse those feature extractors and propose a novel method called random correlation ensemble (RCE) for multi-view ensemble learning. We compare RCE with existing multi-view feature extraction methods including CCA and discriminant CCA (DCCA) which use all cross-view correlations between within-class examples, as well as the trivial ensembles of CCA and DCCA which adopt standard bagging and boosting strategies for ensemble learning. Experimental results on several multi-view data sets validate the effectiveness of the proposed method.     

基于局部流形重构的半监督多视图图像分类

为了在半监督情境下利用多视图特征中的信息提升分类性能,通过最小化输入特征向量的局部重构误差为以输入特征向量为顶点构建的图学习合适的边权重,将其用于半监督学习。通过将最小化输入特征向量的局部重构误差捕获到的输入数据的流形结构应用于半监督学习,有利于提升半监督学习中标签预测的准确性。对于训练样本图像的多视图特征的使用问题,借助于改进的典型相关分析技术学习更具鉴别性的多视图特征,将其有效融合并用于图像分类任务。实验结果表明,该方法能够在半监督情境下充分地挖掘训练样本的多视图特征表示的鉴别信息,有效地完成鉴别任务。     

一种针对多关系数据的半监督协同训练算法

半监督学习是机器学习领域的研究热点。协同训练研究数据有多个特征集时的半监督学习问题。将图表示法引入协同训练,使用多个图结构表示多关系数据。在每个图上进行半监督学习,在多个图之间进行协同学习,使多个图上的学习器对数据的预测一致。创新性地提出一种针对多关系数据的半监督协同训练算法,并从概率角度分析学习过程。在真实数据集上的实验表明,提出的算法处理多关系数据时具有较好的性能。     

多视角数据缺失补全

随着信息技术的快速发展,现实生活中不断涌现出大量的多视角数据,由此应运而生的多视角学习已成为机器学习领域的研究热点.然而,在数据获取过程中,由于收集的难度、高额成本或设备故障等问题,往往导致收集到的多视角数据出现视角缺失,这使得一些多视角学习方法无法有效进行.为此,本文提出了一种基于视角相容性的多视角数据缺失补全方法.通过监督的共享子空间学习,获得与每类多视角数据相对应的共享子空间,从而建立视角相容性判别模型.与此同时,基于共享子空间重构误差等同分布的假设,提出了针对视角缺失的多视角数据的共享表征获取方法,实现多视角缺失数据的预补全.在此基础上,进一步通过多元线性回归实现缺失视角的精确补全.此外,本文还把所提出的视角补全方法拓展到解决含有噪声的多视角数据的降噪问题.在UCI、COIL-20以及人工合成数据集上的实验结果验证了本文算法的有效性.     

Knowledge transfer across different domain data with multiple views

In many real-world applications in the areas of data mining, the distributions of testing data are different from that of training data. And on the other hand, many data are often represented by multiple views which are of importance to learning. However, little work has been done for it. In this paper, we explored to leverage the multi-view information across different domains for knowledge transfer. We proposed a novel transfer learning model which integrates the domain distance and view consistency into a 2-view support vector machine framework, namely DV2S. The objective of DV2S is to find the optimal feature mapping such that under the projections the classification margin is maximized, while both the domain distance and the disagreement between multiple views are minimized simultaneously. Experiments showed that DV2S outperforms a variety of state-of-the-art algorithms.     

基于典型相关分析的多视图降维算法综述

随着数据采集技术的发展,人们获取数据的途径呈多样化,所得到的数据往往具有多个视图,从而形成多视图数据。利用多视图数据不同的信息特征,设计相应的多视图学习策略以提高分类器的性能是多视图学习的研究目标。为更好地利用多视图数据,促进降维算法在实际中的应用,对多视图降维算法进行研究。分析多视图数据和多视图学习,在典型相关分析(CCA)的基础上追溯多视图CCA和核CCA,介绍多视图降维算法从两个视图到多个视图以及从线性到非线性的演化过程,总结各种融入判别信息和近邻信息的多视图降维算法,以更好地学习多视图降维算法。在此基础上,对比分析多视图降维算法的特点及存在的问题,并对未来的研究方向进行展望。     

鲁棒多视图协同完整鉴别子空间学习算法

为了有效地融合多视图信息并使有利于多视图完整子空间学习的视图主导多视图学习,提出了多视图协同完整子空间学习策略。进一步,为了使对象在潜在完整子空间中的完整特征表示具有更好的鉴别能力,将Fisher鉴别分析引入到了多视图完整子空间学习中。Fisher鉴别分析可以在最小化对象的完整特征表示的类内散度的同时最大化对象的完整特征表示的类间散度。将多视图协同完整空间学习策略和Fisher鉴别分析融合在一起,提出了鲁棒多视图协同完整鉴别子空间学习算法。实验结果表明,所提算法能够有效地融合多视图信息并挖掘鉴别信息,是一种有效的多视图完整子空间学习算法。     

面向图像匹配的局部特征提取研究进展

图像匹配作为计算机视觉领域的重要研究方向,广泛应用于图像配准、图像融合、变化检测、视觉导航、3D重建、视觉同时定位与地图构建(SLAM)等领域,精确稳健的局部特征提取是实现其高效处理的前提与关键。以图像匹配研究为导向,从传统特征设计到现代特征学习对局部特征提取方法进行了分类总结,首先,为增强对现代局部特征提取方法的理解,重点介绍了基于传统特征设计的相关方法,接着回顾了基于经典机器学习的方法,搭建起传统方法到深度学习方法的桥梁,最后详细讨论了基于深度学习的现代特征提取方法。针对跨传感器、多视角、不同时段环境下的图像匹配需求,全面分析了各阶段主流方法的优缺点,提出了目前存在的问题与挑战,并给出了相应的研究建议,为相关研究人员全面深入理解图像局部特征提取方法并利用深度学习方法对其进行改进提供基础性参考。     

基于块对角化表示的多视角字典对学习

字典学习作为一种高效的特征学习技术被广泛应用于多视角分类中.现有的多视角字典学习方法大多只利用多视角数据的部分信息,且只学习一种类型的字典.实际上,多视角数据的相关性信息和多样性信息同样重要,且仅考虑一种合成型字典或解析型字典的学习算法不能同时满足处理速度、可解释性以及应用范围的要求.针对上述问题,提出了一种基于块对角...     

多视图动量对比学习算法

比较同一图像不同增强的相似性是对比学习取得显著成果的关键。传统对比学习方法使用了图像的两个不同视图,为了学习到图像更多的信息以提高分类准确率,在MoCo(momentum contrast for unsupervised visual representation learning)的基础上,提出了一种多视图动量对比学习算法。每次迭代中,对于图像的多个数据增强分别使用一个查询编码器和多个动量编码器进行特征提取,使得本次迭代可以使用更多的数据增强和负样本。使用优化的噪声对比估计(InfoNCE)来计算损失,使得查询编码器能得到更有益于下游任务的特征表示。对查询编码器使用梯度回传更新网络,对各动量编码器使用改进的动量更新公式以提高模型的泛化能力。实验结果表明,使用多视图动量对比学习可以有效提高模型的分类准确率。     

基于共享隐空间的多视角SVM

传统的单视角方法对来自不同场景不同形式的多视角样本难以获得较好的分类性能,因此多视角学习成为近年来的热门研究课题并被广泛研究.在多视角学习中,可能存在这样一种特殊现象,即来自不同视角相同类的样本间的差异比来自同一视角不同类的样本间的差异大,这给多视角学习带来很大挑战,并导致多视角学习效果变差.鉴于此,首先利用Parze...     

Force field convergence map and Log-Gabor filter based multi-view ear feature extraction

Based on force field convergence map and Log-Gabor filter, a novel multi-view ear feature extraction approach is proposed. This work first introduces the basic concepts and principles of force field transformation. Then a discussion on why force field convergence map rather than force field transformation is more suitable for multi-view ear feature extraction is given. After getting multi-view ear force field convergence map, Log-Gabor filter is applied to extract multiple scale and multiple orientation features. Finally, to verify the effectiveness of the proposed feature extraction method, different classifiers and different multi-view ear dataset are well utilized, to perform multi-view ear classification task. Experimental results and comparisons show the efficiency and the superiority of the proposed convergence map with the Log-Gabor filter method.