基于格拉斯曼流形子空间融合的多视图聚类

现有的多视图聚类算法大多假设多视图数据点之间为线性关系,且在学习过程中无法保留原始特征空间的局部性;而在欧氏空间中进行子空间融合又过于单调,无法将学习到的子空间表示对齐。针对以上问题,提出了基于格拉斯曼流形融合子空间的多视图聚类算法。首先,将核技巧和局部流形结构学习结合以得到不同视图的子空间表示;然后,在格拉斯曼流形上融合这些子空间表示以得到一致性亲和矩阵;最后,对一致性亲和矩阵执行谱聚类来得到最终的聚类结果,并利用交替方向乘子法（ADMM）来优化所提模型。与核多视图低秩稀疏子空间聚类（KMLRSSC）算法相比,所提算法的聚类精度在MSRCV1、Prokaryotic、Not-Hill数据集上分别提高了20.83个百分点、9.47个百分点和7.33个百分点。实验结果验证了基于格拉斯曼流形融合子空间的多视图聚类算法的有效性和良好性能。     

基于多模态多标记迁移学习的早期阿尔茨海默病诊断

针对当前基于机器学习的早期阿尔茨海默病（AD）诊断中训练样本不足的问题,提出一种基于多模态特征数据的多标记迁移学习方法,并将其应用于早期阿尔茨海默病诊断。所提方法框架主要包括两大模块：多标记迁移学习特征选择模块和多模态多标记分类回归学习器模块。首先,通过稀疏多标记学习模型对分类和回归学习任务进行有效结合;然后,将该模型扩展到来自多个学习领域的训练集,从而构建出多标记迁移学习特征选择模型;接下来,针对异质特征空间的多模态特征数据,采用多核学习技术来组合多模态特征核矩阵;最后,为了构建能同时用于分类与回归的学习模型,提出多标记分类回归学习器,从而构建出多模态多标记分类回归学习器。在国际老年痴呆症数据库（ADNI）进行实验,分类轻度认知功能障碍（MCI）最高平均精度为79.1%,预测神经心理学量表测试评分值最大平均相关系数为0.727。实验结果表明,所提多模态多标记迁移学习方法可以有效利用相关学习领域训练数据,从而提高早期老年痴呆症诊断性能。     

基于Grassmann流形的多聚类特征选择

在无监督聚类特征选择过程中,局部欧氏度量可能置乱局部流形的拓扑结构,影响所选特征的聚类性能。为此,提出一种基于Grassmann流形的多聚类特征选择算法。利用局部主成分分析逼近数据点的切空间,获取局部数据的主要变化方向。根据切空间构造Grassmann流形,通过测地距保留局部数据的流形拓扑结构,以L1范数优化逼近流形拓扑,选择利于聚类的原本数据特征。实验结果验证了该算法的有效性。     

黎曼流形上的保局投影在图像集匹配中的应用

目的提出了黎曼流形上局部结构特征保持的图像集匹配方法。方法该方法使用协方差矩阵建模图像集合,利用对称正定的非奇异协方差矩阵构成黎曼流形上的子空间,将图像集的匹配转化为流形上的点的匹配问题。通过基于协方差矩阵度量学习的核函数将黎曼流形上的协方差矩阵映射到欧几里德空间。不同于其他方法黎曼流形上的鉴别分析方法,考虑到样本分布的局部几何结构,引入了黎曼流形上局部保持的图像集鉴别分析方法,保持样本分布的局部邻域结构的同时提升样本的可分性。结果在基于图像集合的对象识别任务上测试了本文算法,在ETH80和YouTube Celebrities数据库分别进行了对象识别和人脸识别实验,分别达到91.5%和65.31%的识别率。结论实验结果表明,该方法取得了优于其他图像集匹配算法的效果。     

格拉斯曼流形降维及应用研究

视频人脸识别的核心问题是如何准确、高效地构建人脸模型并度量模型的相似性,为此提出一种维数约减的格拉斯曼流形鉴别分析方法以提高集合匹配的性能。首先通过子空间建模图像集合,引入投影映射将格拉斯曼流形上的基本元素表示成对应的投影矩阵。然后,为解决高维矩阵计算开销大以及在小样本条件下不能有效描述样本分布的缺陷,引入二维主成分分析方法对子空间的正交基矩阵降维。通过QR分解正则化降维后的矩阵,得到一个低维、紧致的格拉斯曼流形以获得图像集更好的表达。最后将其投影到高维核空间中进行分类。在公开的视频数据库中的实验结果证明,提出的方法在降低计算开销的同时能够获得较高的正确率,是一种有效的基于集合的对象匹配和人脸识别方法。     

混杂数据的多核几何平均度量学习

在机器学习和模式识别任务中,选择一种合适的距离度量方法是至关重要的.度量学习主要利用判别性信息学习一个马氏距离或相似性度量.然而,大多数现有的度量学习方法都是针对数值型数据的,对于一些有结构的数据（比如符号型数据）,用传统的距离度量来度量两个对象之间的相似性是不合理的;其次,大多数度量学习方法会受到维度的困扰,高维度使得训练时间长,模型的可扩展性差.提出了一种基于几何平均的混杂数据度量学习方法.采用不同的核函数将数值型数据和符号型数据分别映射到可再生核希尔伯特空间,从而避免了特征的高维度带来的负面影响.同时,提出了一个基于几何平均的多核度量学习模型,将混杂数据的度量学习问题转化为求黎曼流形上两个点的中心点问题.在UCI数据集上的实验结果表明,针对混杂数据的多核度量学习方法与现有的度量学习方法相比,在准确性方面展现出更优异的性能.     

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.     

Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning

In this paper, we propose a new feature selection method called kernel fisher discriminant analysis and regression learning based algorithm for unsupervised feature selection. The existing feature selection methods are based on either manifold learning or discriminative techniques, each of which has some shortcomings. Although some studies show the advantages of two-steps method benefiting from both manifold learning and discriminative techniques, a joint formulation has been shown to be more efficient. To do so, we construct a global discriminant objective term of a clustering framework based on the kernel method. We add another term of regression learning into the objective function, which can impose the optimization to select a low-dimensional representation of the original dataset. We use L_2,1-norm of the features to impose a sparse structure upon features, which can result in more discriminative features. We propose an algorithm to solve the optimization problem introduced in this paper. We further discuss convergence, parameter sensitivity, computational complexity, as well as the clustering and classification accuracy of the proposed algorithm. In order to demonstrate the effectiveness of the proposed algorithm, we perform a set of experiments with different available datasets. The results obtained by the proposed algorithm are compared against the state-of-the-art algorithms. These results show that our method outperforms the existing state-of-the-art methods in many cases on different datasets, but the improved performance comes with the cost of increased time complexity.

     

A kernel autoassociator approach to pattern classification.

Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.     

基于RGB-D图像核描述子的物体识别方法

针对传统的颜色-深度（RGB-D）图像物体识别的方法所存在的图像特征学习不全面、特征编码鲁棒性不够等问题,提出了基于核描述子局部约束线性编码（KD-LLC）的RGB-D图像物体识别方法。首先,在图像块间匹配核函数基础上,应用核主成分分析法提取RGB-D图像的3D形状、尺寸、边缘、颜色等多个互补性核描述子;然后,分别对它们进行LLC编码及空间池化处理以形成相应的图像编码向量;最后,把这些图像编码向量融合成具有鲁棒性、区分性的图像表示。基于RGB-D数据集的仿真实验结果表明,作为一种基于人工设计特征的RGB-D图像物体识别方法,由于所提算法综合利用深度图像和RGB图像的多方面特征,而且对传统深度核描述子的采样点选取和紧凑基向量的计算这两方面进行了改进,使得物体类别识别率达到86.8%,实体识别率达到92.7%,比其他同类方法具有更高的识别准确率。     

一种基于核的监督流形学习算法

针对流形学习算法——局部保持映射存在的参数选择及不能进行非线性特征提取的问题,提出一种基于核的监督流形学习算法.该算法作为局部保持映射算法的改进算法用样本类标识信息指导建立局部最近邻图,并在建立局部最近邻图使用无参数的相似度量.利用核方法来解决局部保持映射算法在处理线性不可分问题上的局限性问题.在两个常用数据库上验证本文算法的可行性和有效性.     

Extended Spectral Regression for efficient scene recognition

This paper proposes a novel method based on Spectral Regression (SR) for efficient scene recognition. First, a new SR approach, called Extended Spectral Regression (ESR), is proposed to perform manifold learning on a huge number of data samples. Then, an efficient Bag-of-Words (BOW) based method is developed which employs ESR to encapsulate local visual features with their semantic, spatial, scale, and orientation information for scene recognition. In many applications, such as image classification and multimedia analysis, there are a huge number of low-level feature samples in a training set. It prohibits direct application of SR to perform manifold learning on such dataset. In ESR, we first group the samples into tiny clusters, and then devise an approach to reduce the size of the similarity matrix for graph learning. In this way, the subspace learning on graph Laplacian for a vast dataset is computationally feasible on a personal computer. In the ESR-based scene recognition, we first propose an enhanced low-level feature representation which combines the scale, orientation, spatial position, and local appearance of a local feature. Then, ESR is applied to embed enhanced low-level image features. The ESR-based feature embedding not only generates a low dimension feature representation but also integrates various aspects of low-level features into the compact representation. The bag-of-words is then generated from the embedded features for image classification. The comparative experiments on open benchmark datasets for scene recognition demonstrate that the proposed method outperforms baseline approaches. It is suitable for real-time applications on mobile platforms, e.g. tablets and smart phones.     

基于改进多尺度乘积LoG算子的仿射不变形状匹配算法

目标在成像过程中发生的几何变形多数情况下可用仿射变换来描述。据此,提出一种利用角点进行仿射不变形状匹配的算法。首先引入多尺度乘积LoG(MPLoG)算子检测轮廓角点,并根据角点间距自适应地提取轮廓特征点,从而获取形状关键特征;为解决目标的仿射变形问题,采用Grassmann流形Gr(2,n)来表征和度量两形状之间的相似度;最后通过迭代式序列移位匹配算法来克服Grassmann流形对起始点的依赖并完成形状的匹配。对形状数据进行仿真实验的结果表明,所提算法能够有效地实现形状检索和识别,并对噪声有较强的鲁棒性。     

一种基于LBP特征与流形知识的人脸识别方法

人脸识别是计算机视觉领域的研究热点,应用背景广泛。近年来,流形被认为是视觉感知的基础,流形学习算法被用来发现图像的内在特征。如何利用流形学习后的低维内蕴变量成为相关研究的核心问题。但是利用传统的流形学习算法降维得到的人脸低维特征在可分性上存在一定的不足。此外,流形学习算法对光照和姿态变化敏感。针对这两个问题,提出了一种基于局部二值模式（LBP）和流形知识的人脸识别方法。该方法首先利用LBP算子对人脸图像进行局部特征描述,然后使用流形学习算法获得高维特征数据的低维内蕴变量,并用泰勒展开式近似该流形,获取流形知识,最后利用流形知识估计流形距离来实现人脸识别。实验证明,该方法增强了人脸识别对光照变化的鲁棒性,从而提高了识别性能。     

Human emotion recognition using deep belief network architecture

Recently, deep learning methodologies have become popular to analyse physiological signals in multiple modalities via hierarchical architectures for human emotion recognition. In most of the state-of-the-arts of human emotion recognition, deep learning for emotion classification was used. However, deep learning is mostly effective for deep feature extraction. Therefore, in this research, we applied unsupervised deep belief network (DBN) for depth level feature extraction from fused observations of Electro-Dermal Activity (EDA), Photoplethysmogram (PPG) and Zygomaticus Electromyography (zEMG) sensors signals. Afterwards, the DBN produced features are combined with statistical features of EDA, PPG and zEMG to prepare a feature-fusion vector. The prepared feature vector is then used to classify five basic emotions namely Happy, Relaxed, Disgust, Sad and Neutral. As the emotion classes are not linearly separable from the feature-fusion vector, the Fine Gaussian Support Vector Machine (FGSVM) is used with radial basis function kernel for non-linear classification of human emotions. Our experiments on a public multimodal physiological signal dataset show that the DBN, and FGSVM based model significantly increases the accuracy of emotion recognition rate as compared to the existing state-of-the-art emotion classification techniques.     

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.     

用于阿尔茨海默病诊断的权值分布特征学习

针对当前基于机器学习的早期阿尔茨海默病（AD）诊断中有标记训练样本不足的问题,提出一种基于多模态特征数据的权值分布稀疏特征学习方法,并将其应用于早期阿尔茨海默病的诊断.具体来说,该诊断方法主要包括两大模块：基于权值分布的Lasso特征选择模型（WDL）和大间隔分布分类机模型（LDM）.首先,为了获取多模态特征之间的数据分布信息,对传统Lasso模型进行改进,引入权值分布正则化项,从而构建出基于权值分布的Lasso特征选择模型;然后,为了有效地利用多模态特征之间的数据分布信息,以保持多模态特征之间的互补性,直接采用大间隔分布学习算法训练分类器.选取国际阿尔茨海默症数据库（ADNI）中202个多模态特征的被试者样本进行实验,分类AD最高平均精度为97.5%,分类轻度认知功能障碍（MCI）最高平均精度为83.1%,分类轻度认知功能障碍转化为AD（pMCI）最高平均精度为84.8%.实验结果表明,所提WDL特征学习方法可从串联的多模态特征学到性能更优的特征子集,并能根据权值分布获取多模态特征之间的数据分布信息,从而提高早期阿尔茨海默病诊断的性能.     

低秩约束的非线性属性选择算法

针对高维的数据中往往存在非线性、低秩形式和属性冗余等问题,提出一种基于核函数的属性自表达无监督属性选择算法——低秩约束的非线性属性选择算法（LRNFS）。首先,将每一维的属性映射到高维的核空间上,通过核空间上的线性属性选择去实现低维空间上的非线性属性选择;然后,对自表达形式引入偏差项并对系数矩阵进行低秩与稀疏处理;最后,引入核矩阵的系数向量的稀疏正则化因子来实现属性选择。所提算法中用核矩阵来体现其非线性关系,低秩考虑数据的全局信息进行子空间学习,自表达形式确定属性的重要程度。实验结果表明,相比于基于重新调整的线性平方回归（RLSR）半监督特征选择算法,所提算法进行属性选择之后作分类的准确率提升了2.34%。所提算法解决了数据在低维特征空间上线性不可分的问题,提升了属性选择的准确率。     

一种基于局部流形结构的无监督特征学习方法(英文)

Unsupervised feature selection is fundamental in statistical pattern recognition, and has drawn persistent attention in the past several decades. Recently, much work has shown that feature selection can be formulated as nonlinear dimensionality reduction with discrete constraints. This line of research emphasizes utilizing the manifold learning techniques, where feature selection and learning can be studied based on the manifold assumption in data distribution. Many existing feature selection methods such as Laplacian score, SPEC(spectrum decomposition of graph Laplacian), TR(trace ratio) criterion, MSFS(multi-cluster feature selection) and EVSC(eigenvalue sensitive criterion) apply the basic properties of graph Laplacian, and select the optimal feature subsets which best preserve the manifold structure defined on the graph Laplacian. In this paper, we propose a new feature selection perspective from locally linear embedding(LLE), which is another popular manifold learning method. The main difficulty of using LLE for feature selection is that its optimization involves quadratic programming and eigenvalue decomposition, both of which are continuous procedures and different from discrete feature selection. We prove that the LLE objective can be decomposed with respect to data dimensionalities in the subset selection problem, which also facilitates constructing better coordinates from data using the principal component analysis(PCA) technique. Based on these results, we propose a novel unsupervised feature selection algorithm,called locally linear selection(LLS), to select a feature subset representing the underlying data manifold. The local relationship among samples is computed from the LLE formulation, which is then used to estimate the contribution of each individual feature to the underlying manifold structure. These contributions, represented as LLS scores, are ranked and selected as the candidate solution to feature selection. We further develop a locally linear rotation-selection(LLRS) algorithm which extends LLS to identify the optimal coordinate subset from a new space. Experimental results on real-world datasets show that our method can be more effective than Laplacian eigenmap based feature selection methods.     

Semi-supervised adaptive feature analysis and its application for multimedia understanding

Multimedia understanding for high dimensional data is still a challenging work, due to redundant features, noises and insufficient label information it contains. Graph-based semi-supervised feature learning is an effective approach to address this problem. Nevertheless, Existing graph-based semi-supervised methods usually depend on the pre-constructed Laplacian matrix but rarely modify it in the subsequent classification tasks. In this paper, an adaptive local manifold learning based semi-supervised feature selection is proposed. Compared to the state-of-the-art, the proposed algorithm has two advantages: 1) Adaptive local manifold learning and feature selection are integrated jointly into a single framework, where both the labeled and unlabeled data are utilized. Besides, the correlations between different components are also considered. 2) A group sparsity constraint, i.e. l _2?,?1-norm, is imposed to select the most relevant features. We also apply the proposed algorithm to serval kinds of multimedia understanding applications. Experimental results demonstrate the effectiveness of the proposed algorithm.