Online signature verification based on null component analysis and principal component analysis

This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.     

Finger-vein pattern identification using principal component analysis and the neural network technique

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.     

Coupled principal component analysis

A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.     

Subjective principal component analysis

In this study we deal with the problem of finding subjective principal components for a given set of variables in a data matrix. The principal components are not determined by maximizing their variances; they are specified by a user, who can maximize the absolute values of the correlations between principal components and the variables important to him. The correlation matrix of the variables is the basic information needed in the analysis.The problem is formulated as a multiple criteria problem and solved by using an interactive procedure. The procedure is convenient to use and easy to implement. We have implemented an experimental version on an APPLE III microcomputer. A graphical display is used as an aid in finding the principal components. An illustrative application is presented, too.     

Evaluation of writing motion using principal component analysis and scaling analysis

Artificial Life and Robotics - The control of voluntary movements is a dual structure consisting of cognitive and physical controls; cognitive control, unlike physical control requires attentional...     

A fast pixel mapping algorithm using principal component analysis

Pixel mapping is one of the basic processes in color quantization. In this paper, we shall propose a new algorithm using principal component analysis as a faster approach to pixel mapping. Within much shorter search time, our new scheme can find the nearest color which is identical to the one found using a full search. The idea behind the proposed method is quite simple. First, we compute two principal component directions (PCDs) for the palette. Then, the projected values on PCDs are computed for each color in palette. Finally, the projected values, following the triangular inequality principle, can help us reduce the computation time for finding the nearest color. The experimental results reveal that the proposed scheme is more efficient than the previous work.     

Synthesized articulated behavior using space-temporal on-line principal component analysis

This paper presents a new framework to synthesize humanoid behavior by learning and imitating the behavior of an articulated body using motion capture. The video-based motion capturing method has been developed mainly for analysis of human movement, but is very rarely used to teach or imitate the behavior of an articulated body to a virtual agent in an on-line manner. Using our proposed applications, new behaviors of one agent can be simultaneously analyzed and used to train or imitate another with a novel visual learning methodology. In the on-line learning phase, we propose a new way of synthesizing humanoid behavior based on on-line learning of principal component analysis (PCA) bases of the behavior. Although there are many existing studies which utilize PCA for object/behavior representation, this paper introduces two criteria to determine if the dimension of the subspace is to be expanded or not and applies a Fisher criterion to synthesize new behaviors. The proposed methodology is well-matched to both behavioral training and synthesis, since it is automatically carried out as an on-line long-term learning of humanoid behaviors without the overhead of an expanding learning space. The final outcome of these methodologies is to synthesize multiple humanoid behaviors for the generation of arbitrary behaviors. The experimental results using a humanoid figure and a virtual robot demonstrate the feasibility and merits of this method.     

Online monitoring of batch processes using multi-phase principal component analysis

Unfold Principal Component Analysis (u-PCA) has been successfully applied in the monitoring of batch processes. The traditional online monitoring strategy is based on the same unfolding procedure used for end-of-batch monitoring. This procedure may distort the interval where the process is out of normal operation, with delays in the detection of a fault or in the return to normal operation of a faulty batch. In this paper, a new strategy for the generation of a model specially suited for on-line monitoring is presented. This method is based on the combination of four ideas: mean trajectory subtraction and auto-scaling as preprocessing, variable-wise unfolding, addition of lagged variables to fit the dynamics and multi-phase modelling with multi-phase PCA. Evolving and local models have been included in the comparative analysis of the different approaches.     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

Bayesian compressive principal component analysis

Principal component analysis (PCA) is a widely used method for multivariate data analysis that projects the original high-dimensional data onto a low-dimensional subspace with maximum variance. However, in practice, we would be more likely to obtain a few compressed sensing (CS) measurements than the complete high-dimensional data due to the high cost of data acquisition and storage. In this paper, we propose a novel Bayesian algorithm for learning the solutions of PCA for the original data just from these CS measurements. To this end, we utilize a generative latent variable model incorporated with a structure prior to model both sparsity of the original data and effective dimensionality of the latent space. The proposed algorithm enjoys two important advantages: 1) The effective dimensionality of the latent space can be determined automatically with no need to be pre-specified; 2) The sparsity modeling makes us unnecessary to employ multiple measurement matrices to maintain the original data space but a single one, thus being storage efficient. Experimental results on synthetic and realworld datasets show that the proposed algorithm can accurately learn the solutions of PCA for the original data, which can in turn be applied in reconstruction task with favorable results.     

卷积鲁棒主成分分析

鲁棒主成分分析（RPCA）是一种经典的高维数据分析方法,可从带噪声的观测样本中恢复出原始数据。但是,RPCA能工作的前提是目标数据拥有低秩矩阵结构,不能有效处理实际应用中广泛存在的非低秩数据。研究发现,虽然图像、视频等数据矩阵本身可能不是低秩的,但它们的卷积矩阵通常是低秩的。根据这一原理,提出一种称为卷积鲁棒主成分分析（CRPCA）的新方法,利用卷积矩阵的低秩性对原始数据的结构进行约束,从而实现精确的数据恢复。CPRCA模型的计算过程是一个凸优化问题,通过乘子交替方向法（ADMM）来进行求解。通过对合成数据向量以及真实数据图片、视频序列进行实验,验证了该方法相较于其他算法如RPCA、广义鲁棒主成分分析（GRPCA）以及核鲁棒主成分分析（KRPCA）在处理数据非低秩问题上优越性。     

On Bayesian principal component analysis

A complete Bayesian framework for principal component analysis (PCA) is proposed. Previous model-based approaches to PCA were often based upon a factor analysis model with isotropic Gaussian noise. In contrast to PCA, these approaches do not impose orthogonality constraints. A new model with orthogonality restrictions is proposed. Its approximate Bayesian solution using the variational approximation and results from directional statistics is developed. The Bayesian solution provides two notable results in relation to PCA. The first is uncertainty bounds on principal components (PCs), and the second is an explicit distribution on the number of relevant PCs. The posterior distribution of the PCs is found to be of the von-Mises-Fisher type. This distribution and its associated hypergeometric function, , are studied. Numerical reductions are revealed, leading to a stable and efficient orthogonal variational PCA (OVPCA) algorithm. OVPCA provides the required inferences. Its performance is illustrated in simulation, and for a sequence of medical scintigraphic images.     

Online banking performance evaluation using data envelopment analysis and principal component analysis

This paper presents a hybrid approach to conducting performance measurements for Internet banking by using data envelopment analysis (DEA) and principal components analysis (PCA). For each bank, DEA is applied to compute an aggregated efficiency score based on outputs, such as web metrics and revenue; and inputs, such as equipment, operation cost and employees. The 45 combinations of DEA efficiencies of the studied banks are calculated, and used as a ranking mechanism. PCA is used to apply relative efficiencies among the banks, and to classify them into different groups in terms of operational orientations, i.e., Internet banking and cost efficiency focused orientations. Identification of operational fitness and business orientation of each firm, in this way, will yield insights into understanding the weaknesses and strengths of banks, which are considering moving into Internet banking.     

Online identification of nonlinear system using reduced kernel principal component analysis

The Principal Component Analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets. It is readily performed by solving an eigenvalue problem, or by using iterative algorithms that estimate principal components. This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). Therefore, the PCA technique is tuned twice, first we exploit the Kernel PCA (KPCA) which is a nonlinear extension of the PCA to RKHS as it transforms the input data by a nonlinear mapping into a high-dimensional feature space to which the PCA is performed. Second, we use the Reduced Kernel Principal Component Analysis (RKPCA) to update the principal components that represent the observations selected by the KPCA method.     

一种基于PCA的MPSK信号码元速率估计方法

针对MPSK信号的码元速率估计问题, 研究了有限数据条件下循环谱的谱线特征受到背景色噪声干扰的现象, 提出了一种基于主分量分析(PCA)的循环谱特征码元速率估计方法。PCA变换抑制了信号循环谱中的背景色噪声, 提高了估计精度, 减小了估计方差。仿真表明, 该方法在有限数据条件下具有良好的估计性能, 适用于不同成形滤波系数的MPSK信号。     

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface.     

Is it possible to trace an impending earthquake's occurrence from seismo-ionospheric disturbance using principal component analysis? A study of Japan's Iwate-Miyagi Nairiku earthquake on 13 June 2008

This research uses principal component analysis (PCA) to investigate global ionospheric integrated electron content map (GIM) anomalies corresponding to Japan's Iwate-Miyagi Nairiku earthquake of 13 June 2008 (UTC) (M_w=6.9). The PCA transform is applied to GIMs for 20:00-22:00 on 08, 11, and 12 June 2008 (UTC). To perform the transform, image processing is used to subdivide the GIMs into 100 (36° longitude and 18° latitude) smaller maps to form transform matrices of dimension 2×1. The transform allows principal eigenvalues to be assigned to ionospheric integrated electron content anomalies. Anomalies are represented by large principal eigenvalues (i.e., >0.5 in a normalized set). The possibility of geomagnetic storms and solar flare activity affecting the results is done through examining the Dst index for the corresponding days. The study shows that for the Iwate-Miyagi Nairiku earthquake, PCA possibly determined earthquake-related ionospheric disturbances for the whole region, including the epicenter.     

Nonsupervised classification using the principal component

This paper presents a mathematical model of a nonsupervised two-category classifier with a nonparametric learning method by using the first principal component. On the assumptions that the patterns of each category are clustered and that the mean point of all patterns used lies between the two clusters, the separating hyperplane contains the mean pattern point and is perpendicular to the line governed by the first principal component. The learning algorithm for obtaining the mean pattern vector and the first principal component is described, and also some experimental results on random patterns are presented.