Original Training Samples Research Articles

Two approaches-based L2-SVMs reduced to MEB problems for dialect identification

The recent progress in speech and vision has issued from the increased use of machine learning. Not only does the machine learning provides many useful tools, it also help us to understand existing algorithms and their connections in a new light. As a powerful tool in machine learning, support vector machine SVM leads to an expensive computational cost in the training phase due to the large number of original training samples, while minimal enclosing ball MEB presents limitations dealing with a large dataset. The training computation increases as data size becomes large, hence in this paper, we propose two improved approaches that handle this problem in huge dataset used in different domains. These approaches, based on L2-SVMs reduced to MEB problems result in a reduced data optimally matched to the input demands of different background of systems such as Universal Background Model architectures in language recognition and identification systems. We experiment on speech information based on acoustic shifted delta coefficient feature vectors applied in GMM-based dialect identification system where all data outer the ball defined by MEB are eliminated and the training time is reduced. Further numerical experiments on some real-world datasets show proof of the usefulness of our approaches in the field of data mining.

Computer Network Fault Diagnosis Based On Neural Network

Computer network is one of the world's most important infrastructures in twenty-first Century, network fault diagnosis has become the focus of attention. With the development of artificial intelligence, using the neural network technology into the network fault diagnosis area can play an important role to the advantages of neural network in fault diagnosis. In this paper, the method is widely used, which is combined the self organizing feature map (SOM) neural network and multilayer feedforward neural network (BP): The result of the training samples using SOM neural network clustering algorithm is added to the original training samples and set a certain weight, through iterative update to the weight, in order to improve the convergence the speed of BP neural network. Using computer network fault diagnosis as a practical example for the computer simulation and analysis developes a set of computer network diagnosis system can provide reference and assistance for the work of theory research and application.

Sample pair based sparse representation classification for face recognition

Sparse representation classification, as one of the state-of-the-art classification methods, has been widely studied and successfully applied in face recognition since it was proposed by Wright et al. In this study, we proposed a method to generate virtual available facial images and modified the well-known linear regression classification (LRC) and collaborative representation based classification (CRC) for face recognition. The new method integrates the original and virtual symmetry facial images to form a training sample set of large size. Experimental results show that the proposed method can achieve better performance than most of the competitive face recognition methods, e.g. LRC, CRC, INNC, SRC, RCR, RRC and the method in Xu et al. (2014). This promising performance is mainly attributed to the fact that the sample combination scheme used in the new method can exploit limited original training samples to produce a large number of available training samples and to convey sufficient variations of the original training samples.

Using the original and symmetrical face training samples to perform collaborative representation for face recognition

More training samples are able to reveal more possible variation of the illumination, expression and poses and are consequently beneficial for correct classification. However, in real-world applications, there are usually only a limited number of available training samples. Therefore, it is hard to effectively improve the accuracy of face recognition. The symmetry of face is of great importance to face recognition. In this paper, based on the symmetry of the face, the new mirror training samples are first generate new samples. Then the original training samples and the generated symmetry training samples are, respectively, used to perform collaborative representation based classification method. Finally, the scheme of the score level fusion is adopted to integrate the original training samples and symmetrical face training samples for ultimate face recognition by assigning a larger weight to the original training samples. The experimental results show that the proposed method can classify the face with a high accuracy.

Integrating the original and approximate face images to perform collaborative representation based classification

A limited number of available training samples have become a bottleneck of face recognition system. Due to varying poses, facial expression and illumination, the face image might have various changes in the real world. However, non-sufficient training samples cannot convey comprehensively these possible changes, so it is difficult to improve the accuracy of face recognition. We proposed a novel representation based classification method for face recognition in this paper. This method integrates the original training samples and the corresponding approximate representation for face recognition. Firstly, it generates the approximate representation of each training sample. Secondly, the original training samples and the generated approximate representation samples are respectively used to perform collaborative representation based classification (CRC). Finally, the scheme of the score level fusion, which integrates the original training samples and approximate samples by assigning a larger weight to the original training samples, is proposed for ultimate face recognition. Extensive experiments show that our method has good performance.

Determination of candidate subjects for better recognition of faces

In order to improve the accuracy of face recognition and to solve the problem of various poses, we present an improved collaborative representation classification (CRC) algorithm using original training samples and the corresponding mirror images. First, the mirror images are generated from the original training samples. Second, both original training samples and their mirror images are simultaneously used to represent the test sample via improved collaborative representation. Then, some classes which are “close” to the test sample are coarsely selected as candidate classes. At last, the candidate classes are used to represent the test sample again, and then the class most similar to the test sample can be determined finely. The experimental results show our proposed algorithm has more robustness than the original CRC algorithm and can effectively improve the accuracy of face recognition.

Online sequential reduced kernel extreme learning machine

In this paper, we present an Online Sequential Reduced Kernel Extreme Learning Machine (OS-RKELM). In OS-RKELM, only a small part of the instances in the original training samples is employed for training the kernel neurons, while the output weights are attained analytically. Similar to the Online Sequential Extreme Learning Machine (OS-ELM), OS-RKELM learns data samples in a chunk-by-chunk or one-by-one mode and does not require an archival of the data sample once it has been learned. OS-RKELM also contains few control parameters, thus avoiding the need for cumbersome fine-tuning of the algorithm. OS-RKELM supports a widespread types of kernels as hidden neurons and is capable of addressing the singular problem that arises when the initial training samples are smaller than the neuron size. A comprehensive performance evaluation of the OS-RKELM against other state-of-the-art sequential learning algorithms, including OS-ELM, Large-scale Active Support Vector Machine (LASVM) and Budgeted Stochastic Gradient Descent Support Vector Machine (BSGD) using popular time series, regression and classification benchmarks have been conducted. Experimental results obtained indicate that the proposed OS-RKELM showcases improved prediction accuracy and efficiency over the OS-ELM, LASVM and BSGD in many cases.

Barriers to and Facilitators in the Implementation of Cognitive-Behavioral Therapy for Youth Anxiety in the Community.

The study examined, from the perspective of therapists, the barriers to and facilitators in implementing cognitive-behavioral therapy (CBT) for anxious youths in community settings. Fifty therapists (43% of the original training sample of 115 providers) participated in a follow-up interview two years after training and consultation. They reported on barriers to and facilitators in implementation of CBT for youths with anxiety. Qualitative analyses identified numerous barriers and facilitators, including client factors (for example, motivated clients facilitated the use of CBT, whereas clients with complex issues and numerous psychosocial stressors hindered its use), intervention factors (the structure of CBT helped facilitate its use for some providers, whereas others reported feeling constrained by such structure), and organizational factors (for example, the absence of support within one's institution served as a barrier, whereas supervision supporting the use of CBT facilitated implementation). Findings of this implementation trial align with conceptual implementation frameworks and may guide the tailoring of future implementation efforts in order to overcome barriers and maximize facilitators.

Robust kernel collaborative representation for face recognition

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples

The changes of face images with poses and polarized illuminations increase data uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as representations of the left---right deflection of poses or illuminations of the face. Symmetrical face images generated from the original face images also provide more observations of the face which is useful for improving the accuracy of face recognition. In this paper, to the best of our knowledge, it is the first time that the well-known minimum squared error classification (MSEC) algorithm is used to perform face recognition on an extended face database using synthesized mirror training samples, which is titled as extended minimum squared error classification (EMSEC). By modifying the MSE classification rule, we append the mirror samples to the training set for gaining better classification performance. First, we merge original training samples and mirror samples synthesized from original training samples per subject as mixed training samples. Second, EMSEC algorithm exploits mixed training samples to obtain the projection matrix that can best transform the mixed training samples into predefined class labels. Third, the projection matrix is exploited to simultaneously obtain transform results of the test sample and its nearest neighbor from the mixed training sample set. Finally, we ultimately classify the test sample by combining the transform results of the test sample and the nearest neighbor. As an extension of MSEC, EMSEC reduces the uncertainty of the face observation by auxiliary mirror samples, so that it has better robustness classification performance than traditional MSEC. Experimental results on the ORL, GT, and FERET databases show that EMSEC has better generalization ability than traditional MSEC.

Fuzzy Bidirectional Weighted Sum for Face Recognition

A new method for feature extraction and recognition, namely the fuzzy bidirectional weighted sum criterion (FBWSC) is proposed in this paper. FBWSC defines the row directional fuzzy image optimal image projection matrix. Subsequently, each sample in the original training sample set is transformed using the row directional optimal image pro- jection matrix, and the row directional feature training sample set is obtained. Through the fuzzy distance, the row direc- tional weight can be calculated. Similarly, FBWSC defines the column directional fuzzy image optimal image projection matrix; and then obtains the column directional feature training sample set. The column directional weight can be calcu- lated using the fuzzy distance. Having obtained the row and column directional weight, FBWSC can sum the weight of row and column directional feature training sample sets, and then complete the feature extraction of the original sample data. Experiments on the ORL, FERET and Yale face database show that the proposed FBWSC method for face recogni- tion has high recognition rate.

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

Protein-protein interactions (PPIs) play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC) for PPIs prediction. A group of metasamples are extracted from the original training samples and then use thel1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM) and much higher than naive Bayes (NB), neural networks (NN), andk-nearest neighbor (KNN). The result shows that the MSRC is efficient for PPIs prediction.

A novel SRC fusion method using hierarchical multi-scale LBP and greedy search strategy

As is well known, most of the sparse representation based classification (SRC) schemes are not the standard form of SR model, that is, the dictionary in SRC model is not a strict one compare to sparse representation model. Actually, a strict and standard dictionary will create sparser coefficient, but it might not achieve better classification accuracy in SRC model. Therefore, constructing a rational and optimal dictionary for SRC model is a challenging task for us. Understanding the good performance of such unconventional dictionaries demands new algorithmic and analytical techniques. In this paper, we propose a novel SRC fusion method using hierarchical multi-scale local binary patterns (LBP) and greedy search strategy (SRC-GSLBP) for face recognition. The proposed method involves three aspects: dictionary optimization, training variables selection and classification strategy, sparse coding coefficient decomposing. First of all, we get an optimized dictionary through extracting hierarchical multi-scale LBP features from the original training samples. Second, the training variables selection and classification strategy aim to represent a query sample as a linear combination of the most informative training samples, and exploits an optimal representation of training samples from the classes with major relevant contributions. Instead of eliminating several classes at one time, we choose eliminating classes one by one with greedy search (GS) sparse coding process until the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to perform classification. In the context of the proposed method, an important goal is to select a subset of variables that accomplishes one objective: the provision of a descriptive representation for sparse category knowledge structure. We develop a heuristic learning strategy to achieve this goal. Experimental results conducted on the ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method.

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making (DM) procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition, combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

Noise-free representation based classification and face recognition experiments

The representation based classification has achieved promising performance in high-dimensional pattern classification problems. As we know, in real-world applications the samples are usually corrupted by noise. However, representation based classification can take only noise in the test sample into account and is not able to deal with noise in the training sample, which causes side-effect on the classification result. In order to make the representation based classification more suitable for real-world applications such as face recognition, we propose a new representation based classification method in this paper. This method can effectively and simultaneously reduce noise in the test and training samples. Moreover, the proposed method can reduce noise in both the original and virtual training samples and then exploits them to determine the label of the test sample. The virtual training sample is generated from the original face image and shows possible variation of the face in scale, facial pose and expression. The experimental results show that the proposed method performs very well in face recognition.

Integrating the original face images and “symmetrical faces” to perform face recognition

Using the original and ‘symmetrical face’ training samples to perform representation based face recognition was first proposed in [1]. It simultaneously used the original and ‘symmetrical face’ training samples to perform a two-step classification and achieved an outstanding classification result. However, in [1] the “symmetrical face” is devised only for one method. In this paper, we do some improvements on the basis of [1] and combine this “symmetrical faces” transformation with several representation based methods. We exploit all original training samples, left “symmetrical face” training samples and right “symmetrical face” training samples for classification and use the score fusion for ultimate face recognition. The symmetry of the face is first used to generate new samples, which is different from original face image but can really reflect some possible appearance of the face. It effectively overcomes the problem of non-sufficient training samples. The experimental results show that the proposed scheme can be used to improve a number of traditional representation based methods including those that are not presented in the paper.

Sparse representation for face recognition by discriminative low-rank matrix recovery

This paper proposes a discriminative low-rank representation (DLRR) method for face recognition in which both the training and test samples are corrupted owing to variations in occlusion and disguise. The proposed method extends the sparse representation-based classification algorithm by incorporating the low-rank structure of data representation. The DLRR algorithm recovers a clean dictionary with enhanced discrimination ability from the corrupted training samples for sparse representation. Simultaneously, it learns a low-rank projection matrix to correct corrupted test samples by projecting them onto their corresponding underlying subspaces. The dictionary elements from different classes are encouraged to be as independent as possible by regularizing the structural incoherence of the original training samples. This leads to a compact representation of a corrected test sample by a linear combination of more dictionary elements from the corrected class. The experimental results on benchmark databases show the effectiveness and robustness of our face recognition technique.

Quaternion-based improved LPP method for color face recognition

In recent years, pattern recognition and computer vision have increasingly become the focus of research. Locality preserving projection (LPP) is a very important learning method in these two fields and has been widely used. Using LPP to perform face recognition, we usually can get a high accuracy. However, the face recognition application of LPP suffers from a number of problems and the small sample size is the most famous one. Moreover, though the face image is usually a color image, LPP cannot sufficiently exploit the color and we should first convert the color image into the gray image and then apply LPP to it. Transforming the color image into the gray image will cause a serious loss of image information. In this paper, we first use the quaternion to represent the color pixel. As a result, an original training or test sample can be denoted as a quaternion vector. Then we apply LPP to the quaternion vectors to perform feature extraction for the original training and test samples. The devised quaternion-based improved LPP method is presented in detail. Experimental results show that our method can get a higher classification accuracy than other methods.

Support vector set selection using pulse-coupled neural networks

A candidate set of support vectors is selected by using pulse-coupled neural networks to reduce computational cost in learning phase for support vector machines (SVMs). The size of the candidate set of support vectors selected this way is smaller than that of the original training samples so that the computation complexity in learning process for support vectors machines based on this candidate set is reduced and the learning process is accelerated. On the other hand, the candidate set of support vectors includes almost all support vectors, and the performance of the SVM based on this candidate set matches the performance when the full training samples are used.