Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition

We propose Kernel Self-optimized Locality Preserving Discriminant Analysis (KSLPDA) for feature extraction and recognition. The procedure of KSLPDA is divided into two stages, i.e., one is to solve the optimal expansion of the data-dependent kernel with the proposed kernel self-optimization method, and the second is to seek the optimal projection matrix for dimensionality reduction. Since the optimal parameters of data-dependent kernel are achieved automatically through solving the constraint optimization equation, based on maximum margin criterion and Fisher criterion in the empirical feature space, KSLPDA works well on feature extraction for classification. The comparative experiments show that KSLPDA outperforms PCA, LDA, LPP, supervised LPP and kernel supervised LPP.     

Kernel class-wise locality preserving projection

In the recent years, the pattern recognition community paid more attention to a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel local structure based feature extraction method, called class-wise locality preserving projection (CLPP). CLPP utilizes class information to guide the procedure of feature extraction. In CLPP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and the class information. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Moreover, a kernel version of CLPP namely Kernel CLPP (KCLPP) is developed through applying the kernel trick to CLPP to increase its performance on nonlinear feature extraction. Experiments on ORL face database and YALE face database are performed to test and evaluate the proposed algorithm.     

一种用于特征提取的保局判别分析算法

针对保局投影(LPP)为无监督算法的局限,提出了一种新的监督版的LPP,即保局判别分析(LPDA)算法。LPDA吸收了流形学习算法与最大边界准则(MMC)的共同特点,可以将高维的人脸数据投影到低维子空间,具有能处理新样本与无小样本问题的优点。与现有的多种经典相关方法相比,从Yale, UMIST及MIT 3个人脸数据库的实验结果表明,提出的LPDA算法在降维的同时提取了用于人脸识别的更有效的特征,人脸图像识别性能较好,具有较强的判别分析能力。     

Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems

A new expectation maximization (EM) algorithm for time-critical supervised classification tasks in remote sensing is proposed. Compared to standard EM and other approaches, it has the following advantages: (1) No knowledge about the class distributions is needed. (2) The number of components is estimated. (3) It does not require careful initialization. (4) Singular estimates are avoided due to the ability of pruning components. (5) The best discriminating features are identified simultaneously. (6) The features are identified by incorporating Mahalanobis distances.     

LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

超光谱遥感图像有监督LPP特征提取研究

在局部保留投影(LPP)特征提取算法的基础上,利用样本标签信息提出了一种有监督的局部保留投影算法(SPLPP),该算法的邻接图的权值不仅考虑了LPP算法中的相似性权值,而且加入了监督类的相关权值。SPLPP算法主要步骤是先用PCA去除高维超光谱遥感图像的冗余信息,再把监督机制引入到LPP中,实现图像的特征提取,将高维超光谱遥感图像投影到低维空间中,利于分类。应用SPLPP算法对高维的遥感原始超光谱图像进行特征提取后,利用支持向量机(SVM)和最近邻分类器(KNN)对降维后的遥感图像数据进行分类;并与PCA、LPP、LDA等特征提取算法进行了比较实验。实验表明:结合了LPP局部信息保留能力和全域标签信息的SPLPP算法,有更好的局部信息保留能力和类判别能力,使分类器分类精度更高,分类效果更好。     

Generalized K-L transform based combined feature extraction

In this paper, we combine two kinds of features together by virtue of complex vectors and then use the developed generalized K-L transform (or expansion) for feature extraction. The experiments on NUST603 handwritten Chinese character database and CENPARMI handwritten digit database indicate that the proposed method can improve the recognition rate significantly.     

New method for feature extraction based on fractal behavior

In this paper, a novel approach to feature extraction based on fractal theory is presented as a powerful technique in pattern recognition. This paper presents a new fractal feature that can be applied to extract the feature of two-dimensional objects. It is constructed by a hybrid feature extraction combining wavelet analysis, central projection transformation and fractal theory. New fractal feature and fractal signatures are reported. A multiresolution family of the wavelets is also used to compute information conserving micro-features. We employed a central projection method to reduce the dimensionality of the original input pattern. A wavelet transformation technique to transform the derived pattern into a set of sub-patterns. Its fractal dimension can readily be computed, and to use the fractal dimension as the feature vectors. Moreover, a modified fractal signature is also used to distinguish the distinct handwritten signatures. We expect that the proposed fractal method can also be used for improving the extraction and classification of features in pattern recognition.     

基于监督局部线性嵌入特征提取的高光谱图像分类

高光谱图像的数据维数高、数据量大、数据间高度冗余等特点给图像分类带来困难,为进行有效降维、提高分类精度,提出了一种监督局部线性嵌入(SLLE)非线性流形学习特征提取方法。SLLE算法根据数据先验类标签信息所给出的新距离寻找数据点的k最近邻(NN),新距离使得类内距离小于类间距离,这使得SLLE算法更有利于分类。高光谱图像数据和UCI数据的分类结果表明了该方法的有效性。     

有监督的无参数核局部保持投影及人脸识别

针对发掘人脸图像中的高维非线性结构,将加核及构造无参数近邻图两种思想同时引入到局部保持投影算法中,在有监督的模式下,提出了一种新的有监督的无参数核局部保持投影(Parameter-less Supervised Kernel Locality Preserving Projection,PSKLPP)算法并给出了其推导过程。该算法通过将欧氏距离改为对离群数据更为鲁棒的余弦距离,构造无参数近邻图,利用核方法提取人脸图像中的非线性信息,并将其投影在一个高维非线性空间,运用局部保持投影算法得到一线性映射,有效避免了在计算相似矩阵过程中面临的复杂参数选择问题。在ORL和Yale人脸库上的仿真实验验证了所提算法的有效性。     

多流形判别分析在人脸识别中的研究

局部保持投影LPP(Locality Preserving Projection)是一种有效的非线性降维方法,能够使投影降维后的数据与原输入空间中的相似局部结构保持一致,但是该方法没有充分利用类间样本点的权重等重要信息。为了解决这个问题,提出基于Fisher准则的多流形判别分析FMMDA(Fisher Multi-Manifold Discriminant Analysis)方法。结合Fisher准则训练样本类内拉普拉斯图和样本均值类间拉普拉斯图,既保持了原样本的相似局部结构,又充分地利用了不同类别之间的权重。在ORL及Yale人脸库上验证了该方法的有效性。与其他几种最先进的方法相比,FMMDA方法取得了更好的识别效果。     

Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder

In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and long prediction time. We first propose using an unsupervised three-layer nonnegativity-constrained autoencoder to adaptive search the optimal size of the sliding window, then construct a deep nonnegativity-constrained autoencoder network to automatically extract hidden features of driving behavior to further complete driver identification. The results from the public driving behavior dataset indicate that relative to conventional sparse autoencoder, dropout-autoencoder, random tree, and random forest algorithms, our method can effectively search the optimal size of the sliding time window, and the window size is shortened from the traditional 60s to 30s, which can better preserve the intrinsic information of the data while greatly reducing the data volume. Furthermore, our method can extract more distinctive hidden features that aid the classifier to map out the separating boundaries among the classes more easily. Finally, our method can significantly shorten the prediction time and improve the timeliness under the premise of improving the driver identification performance and reducing the model overfitting.     

A multi-manifold discriminant analysis method for image feature extraction

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance.     

Face recognition using discriminant locality preserving projections based on maximum margin criterion

In this paper, we propose a new discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference, rather than the ratio, between the locality preserving between-class scatter and locality preserving within-class scatter. DLPP/MMC is theoretically elegant and can derive its discriminant vectors from both the range of the locality preserving between-class scatter and the range space of locality preserving within-class scatter. DLPP/MMC can also derive its discriminant vectors from the null space of locality preserving within-class scatter when the parameter of DLPP/MMC approaches +∞. Experiments on the ORL, Yale, FERET, and PIE face databases show the effectiveness of the proposed DLPP/MMC.     

Stochastic neighbor projection on manifold for feature extraction

This paper develops a manifold-oriented stochastic neighbor projection (MSNP) technique for feature extraction. MSNP is designed to find a linear projection for the purpose of capturing the underlying pattern structure of observations that actually lie on a nonlinear manifold. In MSNP, the similarity information of observations is encoded with stochastic neighbor distribution based on geodesic distance metric, then the same distribution is required to be hold in feature space. This learning criterion not only empowers MSNP to extract nonlinear feature through a linear projection, but makes MSNP competitive as well by reason that distribution preservation is more workable and flexible than rigid distance preservation. MSNP is evaluated in three applications: data visualization for faces image, face recognition and palmprint recognition. Experimental results on several benchmark databases suggest that the proposed MSNP provides a unsupervised feature extraction approach with powerful pattern revealing capability for complex manifold data.     

Automatic head and facial feature extraction based on geometry variations

Facial anthropometry plays an important role in ergonomic applications. Most ergonomically designed products depend on stable and accurate human body measurement data. Our research automatically identifies human facial features based on three-dimensional geometric relationships, revealing a total of 67 feature points and 24 feature lines — more than the definitions associated with MPEG-4. In this study, we also verify the replicability, robustness, and accuracy of this feature set. Even with a lower-density point cloud from a non-dedicated head scanner, this method can provide robust results, with 86.6% validity in the 5 mm range. We also analyze the main 31 feature points on the human face, with 96.7% validity of less than 5 mm.     

基于判别正则化的局部保留投影

局部保留投影（Locality preserving projections,LPP）是一种常用的线性化流形学习方法,其通过线性嵌入来保留基于图所描述的流形数据本质结构特征,因此LPP对图的依赖性强,且在嵌入过程中缺少对图描述的进一步分析和挖掘。当图对数据本质结构特征描述不恰当时,LPP在嵌入过程中不易实现流形数据本质结构的有效提取。为了解决这个问题,本文在给定流形数据图描述的条件下,通过引入局部相似度阈值进行局部判别分析,并据此建立判别正则化局部保留投影（简称DRLPP）。该方法能够在现有图描述的条件下,有效突出不同流形结构在线性嵌入空间中的可分性。在人造合成数据集和实际标准数据集上对DRLPP以及相关算法进行对比实验,实验结果证明了DRLPP的有效性。     

一种基于特征提取方法的智能预测算法

针对色织生产调度过程中的一类整经轴数预测问题,提出一种整经轴数智能预测算法．首先基于线性特征提取方法（PCA）和非线性特征提取方法（LLE）对影响整经轴数的多维属性参数进行特征提取;然后采用前向神经网络进行整经轴数预测．数值计算结果表明,所提出的方法能满足实际生产过程整经轴数预测的需要．