Spectral graph features for the classification of graphs and graph sequences

In this paper, the classification power of the eigenvalues of six graph-associated matrices is investigated. Each matrix contains a certain type of geometric/ spatial information, which may be important for the classification process. The performances of the different feature types is evaluated on two data sets: first a benchmark data set for optical character recognition, where the extracted eigenvalues were utilized as feature vectors for multi-class classification using support vector machines. Classification results are presented for all six feature types, as well as for classifier combinations at decision level. For the decision level combination, probabilistic output support vector machines have been applied, with a performance up to 92.4 %. To investigate the power of the spectra for time dependent tasks, too, a second data set was investigated, consisting of human activities in video streams. To model the time dependency, hidden Markov models were utilized and the classification rate reached 98.3 %.     

基于人脸重要特征和SVM的人脸识别方法

提出了一种基于人脸重要特征的人脸识别方法,首先选取人脸的重要特征并将其具体化,对得到的重要特征进行主成分分析,然后用支持向量机(Support Vector Machine,SVM)设计重要特征分类器来确定测试人脸图像中重要特征,同时设计支持向量机(SVM)人脸分类器,确定人脸图像的所属类别.对ORL人脸图像数据库进行仿真实验,结果表明,该方法要优于一般的基于整体特征的人脸识别方法并有较强的鲁棒性.     

Structure control classification and optimization model of hollow carbon nanosphere core polymer particle based on improved differential evolution support vector machine

The structure of core polymer particle is an important index of efficiency in hollow carbon nanosphere. How to control and optimize the structure of core polymer particle has been investigated using pattern recognition method in this research. A novel method of pattern recognition material design based on differential evolution support vector machine was proposed. The control model was established and software was adopted to carry out a digital simulation for the model. Using the model, we found the control criteria and optimized conditions for pore structure of composite polymer. Then, the results are compared to other classification methodologies. Experimental results show this model has higher classification accuracy in most of data sets. Experimental and dynamics results show that the properties of hollow carbon nanosphere have been greatly improved.     

A reference model for customer-centric data mining with support vector machines

Supervised classification is an important part of corporate data mining to support decision making in customer-centric planning tasks. The paper proposes a hierarchical reference model for support vector machine based classification within this discipline. The approach balances the conflicting goals of transparent yet accurate models and compares favourably to alternative classifiers in a large-scale empirical evaluation in real-world customer relationship management applications. Recent advances in support vector machine oriented research are incorporated to approach feature, instance and model selection in a unified framework.     

Discriminant Analysis in Schizophrenia based on Neural Network

In this paper, we use neural network to classify schizophrenia patients and healthy control subjects. Based on 4005 high dimensions feature space consist of functional connectivity about 63 schizophrenic patients and 57 healthy control as the original data, attempting to try different dimensionality reduction methods, different neural network model to find the optimal classification model. The results show that using the Mann-Whitney U test to select the more discrimination features as input and using Elman neural network model for classification to get the best results, can reach a highest accuracy of 94.17%, with the sensitivity being 92.06% and the specificity being 96.49%. For the best classification neural network model, we identified 34 consensus functional connectivities that exhibit high discriminative power in classification, which includes 26 brain regions, particularly in the thalamus regions corresponding to the maximum number of functional connectivity edges, followed by the cingulate gyrus and frontal region.     

Sliced Coordinate Analysis for Effective Dimension Reduction and Nonlinear Extensions

Sliced inverse regression (SIR) is an important method for reducing the dimensionality of input variables. Its goal is to estimate the effective dimension reduction directions. In classification settings, SIR is closely related to Fisher discriminant analysis. Motivated by reproducing kernel theory, we propose a notion of nonlinear effective dimension reduction and develop a nonlinear extension of SIR called kernel SIR (KSIR). Both SIR and KSIR are based on principal component analysis. Alternatively, based on principal coordinate analysis, we propose the dual versions of SIR and KSIR, which we refer to as sliced coordinate analysis (SCA) and kernel sliced coordinate analysis (KSCA), respectively. In the classification setting, we also call them discriminant coordinate analysis and kernel discriminant coordinate analysis. The computational complexities of SIR and KSIR rely on the dimensionality of the input vector and the number of input vectors, respectively, while those of SCA and KSCA both rely on the number of slices in the output. Thus, SCA and KSCA are very efficient dimension reduction methods.     

An Automatic User-Recognition Approach Within a Reputation System Using a Nonlinear Hausdorff-Derived Metric

In this paper, we provide an automatic unsupervised recognition technique for Web community user reputations that uses a special nonlinear metric. First we describe the general framework for reputation systems. Then, we propose a feature extraction approach for the reputation system users. The resulting feature vectors (reputations) are clustered with an unsupervised classification algorithm using a nonlinear distance, derived from the Hausdorff metric for sets.     

Ensemble feature selection for high dimensional data: a new method and a comparative study

The curse of dimensionality is based on the fact that high dimensional data is often difficult to work with. A large number of features can increase the noise of the data and thus the error of a learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the data dimensionality. Different feature selection algorithms may yield feature subsets that can be considered local optima in the space of feature subsets. Ensemble feature selection combines independent feature subsets and might give a better approximation to the optimal subset of features. We propose an ensemble feature selection approach based on feature selectors’ reliability assessment. It aims at providing a unique and stable feature selection without ignoring the predictive accuracy aspect. A classification algorithm is used as an evaluator to assign a confidence to features selected by ensemble members based on their associated classification performance. We compare our proposed approach to several existing techniques and to individual feature selection algorithms. Results show that our approach often improves classification performance and feature selection stability for high dimensional data sets.     

Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

Credit applicants are assigned to good or bad risk classes according to their record of defaulting. Each applicant is described by a high-dimensional input vector of situational characteristics and by an associated class label. A statistical model, which maps the inputs to the labels, can decide whether a new credit applicant should be accepted or rejected, by predicting the class label given the new inputs. Support vector machines (SVM) from statistical learning theory can build such models from the data, requiring extremely weak prior assumptions about the model structure. Furthermore, SVM divide a set of labelled credit applicants into subsets of ‘typical’ and ‘critical’ patterns. The correct class label of a typical pattern is usually very easy to predict, even with linear classification methods. Such patterns do not contain much information about the classification boundary. The critical patterns (the support vectors) contain the less trivial training examples. For instance, linear discriminant analysis with prior training subset selection via SVM also leads to improved generalization. Using non-linear SVM, more ‘surprising’ critical regions may be detected, but owing to the relative sparseness of the data, this potential seems to be limited in credit scoring practice.     

Online streaming feature selection using rough sets

Feature Selection (FS) is an important pre-processing step in data mining and classification tasks. The aim of FS is to select a small subset of most important and discriminative features. All the traditional feature selection methods assume that the entire input feature set is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with time as new features stream in. A critical challenge for online streaming feature selection (OSFS) is the unavailability of the entire feature set before learning starts. Several efforts have been made to address the OSFS problem, however they all need some prior knowledge about the entire feature space to select informative features. In this paper, the OSFS problem is considered from the rough sets (RS) perspective and a new OSFS algorithm, called OS-NRRSAR-SA, is proposed. The main motivation for this consideration is that RS-based data mining does not require any domain knowledge other than the given dataset. The proposed algorithm uses the classical significance analysis concepts in RS theory to control the unknown feature space in OSFS problems. This algorithm is evaluated extensively on several high-dimensional datasets in terms of compactness, classification accuracy, run-time, and robustness against noises. Experimental results demonstrate that the algorithm achieves better results than existing OSFS algorithms, in every way.     

基于GA-PLS和AdaBoost的故障诊断方法

提出一种基于GA-PLS和AdaBoost的液压系统故障诊断方法。该方法用遗传算法与偏最小二乘法相结合(Genetic algorithm-partial least squares,GA-PLS)的算法对初始特征向量进行筛选,提取出与故障信息相关程度高的特征向量,把该特征向量作为输入,运用AdaBoost(Adaptive Boost)方法建立分类器,以识别液压系统的工作状态和故障类型。对实验数据分析的结果说明,该方法能准确地选择出特征向量,并有效地应用于液压系统的故障诊断。     

Nonlinear fusion for face recognition using fuzzy integral

Face recognition based only on the visual spectrum is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human face is considered as a promising alternative to visible imagery due to its relative insensitive to illumination changes. However, IR has its own limitations. In order to fuse information from the two modalities to achieve better result, we propose a new fusion recognition scheme based on nonlinear decision fusion, using fuzzy integral to fuse the objective evidence supplied by each modality. The scheme also employs independent component analysis (ICA) for feature extraction and support vector machines (SVMs) for classification evidence. Recognition rate is used to evaluate the proposed scheme. Experimental results show the scheme improves recognition performance substantially.     

Genetic programming for feature construction and selection in classification on high-dimensional data

Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance.     

奇异类内离差矩阵条件下的Fisher最优判据

特征提取是模式分类与识别的重要环节，Fisher最优判据是特征提取的基本方法之一．本文提出了一种计算奇异类内离差矩阵条件下Fisher最优判据的新方法，并给出了计算步骤．     

Music and timbre segmentation by recursive constrained K-means clustering

Clustering of features generated of musical sound recordings proved to be beneficial for further classification tasks such as instrument recognition (Ligges and Krey in Comput Stat 26(2):279–291, 2011). We propose to use order constrained solutions in K-means clustering to stabilize the results and improve the interpretability of the clustering. With this method a further improvement of the misclassification error in the aforementioned instrument recognition task is possible. Using order constrained K-means the musical structure of a whole piece of popular music can be extracted automatically. Visualizing the distances of the feature vectors through a self distance matrix allows for an easy visual verification of the result. For the estimation of the right number of clusters, we propose to calculate the adjusted Rand indices of bootstrap samples of the data and base the decision on the minimum of a robust version of the coefficient of variation. In addition to the average stability (measured through the adjusted Rand index) this approach takes the variation between the different bootstrap samples into account.     

A recognition problem for a vector alphabet generating a sequence with a quasiperiodic structure

An off-line recognition problem is analyzed for a vector alphabet generating sequences with quasiperiodic vector fragments that coincide with alphabet vectors. It is shown that the solution of this problem reduces to solving a special optimization problem. It is proved that the problem considered is solvable in polynomial time, and an algorithm for its exact solution is justified. The algorithm ensures the maximum likelihood recognition of a vector alphabet for the case of additive noise which is a Gaussian sequence of independent random values having an identical distribution.     

Recognition of a sequence as a structure containing series of recurring vectors from an alphabet

A polynomial-time algorithm is designed for finding an optimal solution of a discrete optimization problem to which a pattern recognition problem is reduced, namely, the noise-proof recognition of a sequence as a structure consisting of contiguous subsequences in the form of series of identical nonzero vectors from an alphabet of vectors in the Euclidean space that alternate with zero vectors.     

稀疏降维的近似凸壳覆盖一类分类器构造

针对高维数据集常常存在冗余和维数灾难,在其上直接构造覆盖模型难以充分反映数据分布信息的问题,提出一种基于稀疏降维近似凸壳覆盖模型.首先采用同伦算法求解稀疏表示中l_1优化问题,通过稀疏约束自动获取合理近邻数并构建图,再通过LPP(Locality Preserving Projections)来进行局部保持投影,进而实现对高维空间快速有效地降维,最后在低维空间通过构造近似凸壳覆盖实现一类分类.在UCI数据库,MNIST手写体数据库和MIT-CBCL人脸识别数据库上的实验结果证实了方法的有效性,与现有的一类分类算法相比,提出的覆盖模型具有更高的分类正确率.     

A Semi-Supervised Learning Algorithm for Growing Neural Gas in Face Recognition

In many classification applications and face recognition tasks, there exist unlabelled data available for training along with labelled samples. The use of unlabelled data can improve the performance of a classifier. In this paper, a semi-supervised growing neural gas is proposed for learning with such partly labelled datasets in face recognition applications. The classifier is first trained on the labelled data and then gradually unlabelled data is classified and added to the training data. The classifier is retrained; and so on. The proposed iterative algorithm conforms to the EM framework and is demonstrated, on both artificial and real datasets, to significantly boost the classification rate with the use of unlabelled data. The improvement is particularly great when the labelled dataset is small. Comparison with support vector machine classifiers is also given. The algorithm is computationally efficient and easy to implement.     

雷达船目标的模糊智能识别

模糊集理论和技术给复杂的模糊背景下的模式识别问题提供了一种简易有效的处理手段。基于模糊集理论,定量地分析和处理识别过程中的各种不确定性信息,以提高识别系统的可靠性和智能度,便是我们研究舰船雷达目标识别系统中识别算法的一重要内容。本文通过舰船雷达目标识别这一典型的模糊模式识别问题,系统地论述了模糊模式识别系统的一般性框架、模糊特征抽取和隶属函数的建立、模糊特征选择和匹配分类、层次化模糊分类系统构造等一系列关键性问题。本文提出的各种方法具有通用性,已建立的识别系统在试验中具有良好的目标识别能力。