Sparse two-dimensional local discriminant projections for feature extraction

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) and two-dimensional discriminant locality preserving projections (2DDLPP) were recently proposed to directly extract features form 2D face matrices to improve the performance of two-dimensional locality preserving projections (2DLPP). But all of them require a high computational cost and the learned transform matrices lack intuitive and semantic interpretations. In this paper, we propose a novel method called sparse two-dimensional locality discriminant projections (S2DLDP), which is a sparse extension of graph-based image feature extraction method. S2DLDP combines the spectral analysis and L₁-norm regression using the Elastic Net to learn the sparse projections. Differing from the existing 2D methods such as 2DLPP, 2DDLP and 2DLGEDA, S2DLDP can learn the sparse 2D face profile subspaces (also called sparsefaces), which give an intuitive, semantic and interpretable feature subspace for face representation. We point out that using S2DLDP for face feature extraction is, in essence, to project the 2D face images on the semantic face profile subspaces, on which face recognition is also performed. Experiments on Yale, ORL and AR face databases show the efficiency and effectiveness of S2DLDP.     

A collaborative representation based projections method for feature extraction

In graph embedding based methods, we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm (e.g. LLE). It is difficult and unstable to manually choose the nearest neighbors in high dimensional space. So how to automatically construct a graph is very important. In this paper, first, we give a L2-graph like L1-graph. L2-graph calculates the edge weights using the total samples, avoiding manually choosing the nearest neighbors; second, a L2-graph based feature extraction method is presented, called collaborative representation based projections (CRP). Like SPP, CRP aims to preserve the collaborative representation based reconstruction relationship of data. CRP utilizes a L2 norm graph to characterize the local compactness information. CRP maximizes the ratio between the total separability information and the local compactness information to seek the optimal projection matrix. CRP is much faster than SPP since CRP calculates the objective function with L2 norm while SPP calculate the objective function with L1 norm. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that CRP works well in feature extraction and leads to a good recognition performance.     

Two-dimensional bilinear preserving projections for image feature extraction and classification

Two-dimensional locality preserving projections (2DLPP) was recently proposed to extract features directly from image matrices based on locality preserving criterion. A significant drawback of 2DLPP is that it only works on one direction (left or right) to reduce the dimensionality of the image matrices and thus too many coefficients are needed for image representation in low-dimensional subspace. In this paper, we propose a novel method called two-dimensional bilinear preserving projections (2DBPP) for image feature extraction. We generalized the image-based (2D-based) feature extraction techniques into bilinear cases, in which 2DLPP is a special case of our proposed method. In order to obtain the bilinear projections, we proposed an iteration method by solving the corresponding generalized eigen-equations. Moreover, analyses show that 2DBPP has stronger locality preserving abilities than 2DLPP. By using the label information and defining different local neighborhood graphs, the proposed framework is further extended to supervised case. Experiments on three databases show that 2DBPP and its supervised extension are superior to some other image-based state-of-the-art techniques.     

Spectral feature projections that maximize Shannon mutual information with class labels

Determining optimal subspace projections that can maintain task-relevant information in the data is an important problem in machine learning and pattern recognition. In this paper, we propose a nonparametric nonlinear subspace projection technique that maintains class separability maximally under the Shannon mutual information (MI) criterion. Employing kernel density estimates for nonparametric estimation of MI makes possible an interesting marriage of kernel density estimation-based information theoretic methods and kernel machines, which have the ability to determine nonparametric nonlinear solutions for difficult problems in machine learning. Significant computational savings are achieved by translating the definition of the desired projection into the kernel-induced feature space, which leads to obtain analytical solution.     

基于特征面的分层特征识别

为降低特征识别的复杂度,提出基于特征实体、特征实面和特征虚面概念的层次性特征分类方法.通过构造2类神经网络输入矩阵,利用神经网络在特征识别中所具有的优势,实现基于特征面的分层特征识别方法.实例表明：该方法在识别去除材料的特征时比较有效,但识别特征的范围受到一定限制.     

基于特征相关的谱特征选择算法

针对传统的谱特征选择算法只考虑单特征的重要性,将特征之间的统计相关性引入到传统谱分析中,构造了基于特征相关的谱特征选择模型。首先利用Laplacian Score找出最核心的一个特征作为已选特征,然后设计了新的特征组区分能力目标函数,采用前向贪心搜索策略依次评价候选特征,并选中使目标函数最小的候选特征加入到已选特征。该算法不仅考虑了特征重要性,而且充分考虑了特征之间的关联性,最后在2个不同分类器和8个UCI数据集上的实验结果表明:该算法不仅提高了特征子集的分类性能,而且获得较高的分类精度下所需特征子集的数量较少。     

Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set

This paper presents a new method for image feature extraction, namely, the fuzzy 2D discriminant locality preserving projections (F2DDLPP) based on the 2D discriminant locality preserving projections (2DDLPP) and fuzzy set theory. Firstly, we calculate the membership degree matrix by fuzzy k-nearest neighbor (FKNN), then we incorporate the membership degree matrix into the definition of the intra-class scatter matrix and inter-class scatter matrix, respectively. Secondly, we can get the fuzzy intra-class scatter matrix and fuzzy inter-class scatter matrix, respectively. The FKNN is implemented to achieve the distribution information of original samples, and this information is utilized to redefine corresponding scatter matrices. So, F2DDLPP can extract discriminative features from overlapping (outlier) samples which is different to the conventional 2DDLPP. Finally, Experiments on the Yale, ORL face databases, USPS database and PolyU palmprint database are demonstrated to verify the effectiveness of the proposed algorithm.     

一种基于特征聚类的特征选择方法

特征选择是数据挖掘和机器学习领域中一种常用的数据预处理技术。在无监督学习环境下,定义了一种特征平均相关度的度量方法,并在此基础上提出了一种基于特征聚类的特征选择方法 FSFC。该方法利用聚类算法在不同子空间中搜索簇群,使具有较强依赖关系（存在冗余性）的特征被划分到同一个簇群中,然后从每一个簇群中挑选具有代表性的子集共同构成特征子集,最终达到去除不相关特征和冗余特征的目的。在 UCI 数据集上的实验结果表明,FSFC 方法与几种经典的有监督特征选择方法具有相当的特征约减效果和分类性能。     

基于特征相关的偏最小二乘特征选择方法

针对传统的偏最小二乘法只考虑单特征的重要性以及特征之间存在冗余和多重共线性等问题，将特征之间的统计相关性引入到传统的偏最小二乘分析中，构造了一种基于特征相关的偏最小二乘模型。首先利用特征相关度对特征进行评估预选出特征组，然后将其放入偏最小二乘模型中进行训练，评估该特征组是否可取。结合前向贪心搜索策略依次评价候选特征，并选中使目标函数最小的候选特征加入到已选特征。分别采用麻杏石甘汤君药止咳、平喘和UCI数据集进行分析处理，实验结果表明，该特征选择方法能较好寻找较优的特征组。     

Localization and classification based on projections

Due to the loss of range information, projections as input data for a 3-D object recognition algorithm are expected to increase the computational complexity. In this work, however, we demonstrate that this deficiency carries potential for complexity reduction of major vision problems. We show that projections provide a reduction of feature dimensions, and lead to structures exhibiting simple combinatorial properties. The theoretical framework is embedded in a probabilistic setting which deals with uncertainties and variations of observed features. In statistics marginal densities and the assumption of independency prove to be the key tools when one encounters projections. The examples discussed in this paper include feature matching, pose estimation as well as classification of 3-D objects. The final experimental evaluation demonstrates the practical importance of the marginalization concept and independency assumptions.     

基于特征聚类集成技术的在线特征选择

针对既有历史数据又有流特征的全新应用场景,提出了一种基于组特征选择和流特征的在线特征选择算法。在对历史数据的组特征选择阶段,为了弥补单一聚类算法的不足,引入聚类集成的思想。先利用k-means方法通过多次聚类得到一个聚类集体,在集成阶段再利用层次聚类算法对聚类集体进行集成得到最终的结果。在对流特征数据的在线特征选择阶段,对组构造产生的特征组通过探讨特征间的相关性来更新特征组,最终通过组变换获得特征子集。实验结果表明,所提算法能有效应对全新场景下的在线特征选择问题,并且有很好的分类性能。     

基于特征聚类的封装特征选择算法

针对多维数据集,为得到一个最优特征子集,提出一种基于特征聚类的封装式特征选择算法。在初始阶段,利用三支决策理论动态地将原始特征集划分为若干特征子空间,通过特征聚类算法对每个特征子空间内的特征进行聚类;从每个特征类簇里挑选代表特征,利用邻域互信息对剩余特征进行降序排序并依次迭代选择,使用封装器评估该特征是否应该被选择,可得到一个具有最低分类错误率的最优特征子集。在UCI数据集上的实验结果表明,相较于其它特征选择算法,该算法能有效地提高各数据集在libSVM、J48、Nave Bayes以及KNN分类器上的分类准确率。     

语义特征造型系统中特征模型转换的研究

通过对新一代的语义特征模型的分析,参考Bidarra等人的研究成果,运用有效性标准,确立语义特征造型中特征转换的方法,在采用数学方法基础上,提出了怎样在数学模型中把设计特征表示转换成加工特征表示。设计过程中的设计特征由一组具有几何特征的面和一组属性（包括维数和具体特征）表示。加工过程中的加工特征由许多面和这些加工操作中面面之间有意义的关系来表示。特征转换中采用数学方法,能够在集合运算中形成加工特征,特征相交这种困难问题也可用数学描述,并且理论上能够转换,从而提高产品模型的可编辑性和易编辑性。     

基于全局特征和尺度不变特征转换特征融合的医学图像检索

特征提取是图像检索或图像配准的关键步骤,针对单一特征不能很好地表述图像的问题, 根据医学图像的特点,提出了一种融合全局特征和局部特征的医学图像检索算法。首先在研究单一特征医学图像检索算法的基础上, 提出了融合全局特征和相关反馈的检索算法;其次对尺度不变特征转换(SIFT)特征进行了优化,提出了改进的SIFT 特征提取算法和匹配算法;最后,为了保证结果的准确性并改进检索效果,采用了融合局部特征的方法逐步求精。通过对标准临床数字式X射线成像(DR)图像数据库的实验研究表明,该算法应用在医学图像的检索中有较好的结果。     

基于SIFT和角点特征的商标检索算法

针对基于尺度不变特征变换(SIFT)的商标图像检索对于高度相似的反转商标图像容易出现漏检现象,提出了一种基于SIFT和角点特征的商标图像检索方法,该方法利用商标图像的局部角点特征弥补了SIFT特征的不足。实验结果表明,与现有方法相比,该方法保留了SIFT特征对遮挡、扭曲、噪声等干扰具有很强的抵抗力的优点外,同时增强了算法对图像形状的描述能力,表现出了更好的检索能力和性能。     

缺失标记下基于类属属性的多标记特征选择

多标记特征选择已在图像分类、疾病诊断等领域得到广泛应用;然而,现实中数据的标记空间往往存在部分标记缺失的问题,这破坏了标记间的结构性和关联性,使得学习算法难以准确地选择重要特征。针对此问题,提出一种缺失标记下基于类属属性的多标记特征选择（MFSLML）算法。首先,通过利用稀疏学习方法获取每个类标记的类属属性;同时基于线性回归模型构建类属属性与标记的映射关系,以用于恢复缺失标记;最后,选取7组数据集以及4个评价指标进行实验。实验结果表明：相比基于最大依赖度和最小冗余度的多标记特征选择算法（MDMR）和基于特征交互的多标记特征选择算法（MFML）等一些先进的多标记特征选择算法,MFSLML在平均查准率指标上能够提升4.61~5.5个百分点,由此可见MFSLML具有更优的分类性能。     

基于视觉与结构谱特征融合的视频检索

视频的时序结构特征是视频的重要特征。提出了一种基于DTW和图谱理论的视频结构特征提取与表示方法,并将这种结构特征融合视觉特征用于视频的镜头检索。根据镜头分割中的帧差曲线,使用DTW原理得到视频镜头特征帧,以镜头特征帧作为图的顶点,以特征帧间的关系作为图的边,生成镜头关系图,分解图的邻接矩阵,得到镜头结构谱,融合镜头的视觉特征得到最终的检索结果。实验结果表明,视频的结构谱特征可以有效地表示视频的结构特征,在视频检索中也是有效的。