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This paper aims to provide a new method of visualizing high-dimensional data classification by employing principal component analysis (PCA) and support vector machine (SVM). In this method, PCA is adopted to reduce the dimension of high-dimensional data, and then SVM is used for the data classification process. At last, the classified result is projected to two-dimension mapping. The method can visualize...
Categorical data exist in many domains, such as text data, gene sequences, or data from Census Bureau. While such data are easy for human interpretation, they cannot be directly used by many classification methods, such as support vector machines and others, which require underlying data to be represented in a numerical format. To date, most existing learning methods convert categorical data into...
Deep convolutional neural networks (CNNs) based face recognition approaches have been dominating the field. The success of CNNs is attributed to their ability to learn rich image representations. But training CNNs relies on estimating millions of parameters and requires a very large number of annotated training images. A widely-used alternative is to fine-tune the CNN that has been pre-trained using...
Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture...
In this paper we propose a scalable face image compression algorithm based on Principal Component Analysis (PCA) and Entropy Coding. By using PCA and some training face image patterns, we can extract the most representative eigen-image of human faces. To reduce the coding complexity as well as to achieve a higher compression ratio, only the first term of the extracted eigen-images will be used for...
A plethora of disorders are found in human oral mucosa. A variety and huge number of lesions and diseases in human oral mucosa have been clinically identified and classified. Most lesions have the possibility to develop into oral cancer. The initial diagnosis of oral cancer is to inspect the ocular regions carefully and register the oral cavity of the patient as true-color digital images. The decision...
It is important to cut down the erection time and the operation guidance by studying the shield machine tool failure. In this paper, an ACO-BP algorithm based tool failure prediction model is established by utilizing the nonlinear mapping characteristics of neural network and mining data characteristics from the subway. According to the practical problems, the dependent variables and the independent...
Personality is the defining essence of an individual as it guides the way we think, act and interpret external stimuli. Classification of personality is important as it can serves as a framework in the job assignment task, particularly, in the high risk job including the Police Force. There are many attributes of individual traits but not all of them can be used to indicate individual personality...
We present the key steps in the dynamogram classification algorithm development. These are data processing, procedures of generation and selection of features, constructing of a neural network classifier and estimation of its work quality. To estimate the possibility to single out complex defects (subclasses), we analyzed the structure of the input pattern sample with the aid of clusterization algorithms...
The objective of this paper is to extend the applicability of the GLR method to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Kernel partial least squares (KPLS) models have been widely used to represent such systems. Therefore, in this paper, kernel PLS-based GLR method will be utilized in practice to improve...
This paper presents two different implementations for recognition of handwritten numerals using a high performance autoencoder and Principal Component Analysis (PCA) by making use of neural networks. Different from other approaches, the non-linear mapping capability of neural networks is used extensively here. The implementation involves the deployment of a neural network, and the use of an auto encoder...
Some challenges of developing face recognition system is to train examples of different poses, illuminations, contrast and background conditions. Even though if the system works okay with the tested data set; it fails big time to perform accurately with new data set with different attributes. This manuscript is focusing on the mentioned problem statement by deploying PCA and implementing the concept...
A real-time fault detection and isolation strategy for gas sensor arrays is presented in this paper. To improve the efficiency of the basic kernel principal component analysis (KPCA) algorithm, a sample selection method is utilized to extract the approximate basis of the entire training sample set, and reduce the time consumption on the calculation of the kernel matrix. Further, a novel algorithm...
One of the main challenges in pattern recognition is handling variations in pose, which has been addressed in the past using exhaustive training, increasingly complex neural network architectures, or state space transformations, but often with limits on pose variation. The solution presented here implements complete pose invariance by estimating affine transform parameters and then registering samples...
Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation...
Deep Learning methods have proven to be very successful in classifying large data sets of high feature dimensionality. However, their success usually implies very long training times. In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring...
We describe the reconstruction of bifurcation diagrams using an extreme learning machine with a pruning algorithm. We can reconstruct the bifurcation diagram from only some time-series data by using a neural network. However, the reconstruction accuracy is influenced by the structure of the neural network. To improve reconstruction accuracy we apply a pruning algorithm to the neural network used for...
Industrial big data has created a challenge for data measurement, detection, and processing. This paper shows that support vector machine (SVM) is extremely useful in detecting fault information in modern complex industrial processes. With a pilot plant of Continuous Stirred Tank Heater (CSTH) process, the SVM method with radial basis function (RBF) kernels is tested on the CSTH database and compared...
An adaptive neuro-fuzzy inference system-based partial least squares (ANFIS-PLS) method was proposed for monitoring nonlinear processes. The ANFIS was used as a predictor to represent the nonlinear relationship between input and output score variables in each inner loop of PLS, and fuzzy c-means clustering was employed to determine the number of fuzzy rules. Moreover, the hybrid learning algorithm...
Reliability assessment is s a critical consideration in regards to equipments proactive maintenance. Selecting the features which can accurately reflect the performance degradation process as the inputs of the reliability assessment model is the precondition of accurate reliability assessment. A novel method based on principal component analysis (PCA) and weibull proportional hazards model (WPHM)...
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