Orthogonal Locality Preserving Projection Research Articles

A novel vibro-acoustic fault diagnosis method of rolling bearings via entropy-weighted nuisance attribute projection and orthogonal locality preserving projections under various operating conditions

Accurate fault pattern recognition under various operating conditions is a huge challenge for vibro-acoustic fault diagnosis of rolling bearings. Traditional feature fusion methods are difficult to extract sensitive features related to faults under various operating conditions. To solve the problem, a novel feature fusion method is proposed in this paper based on entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP). Specifically, the method mainly includes three steps: feature extraction, alleviating interference of operating condition, and fusion feature. First, features are extracted from the acquired sound signals. Second, by introducing the covariance matrix of the acquired signals and fuzzy entropy to improve the weighted matrix of nuisance attribute projection (NAP), the EWNAP is proposed to reduce the impact of operating conditions in these extracted features. In the end, OLPP is adopted to fuse features and obtain sensitive features. The clustering performance of the proposed method is quantitatively described by an evaluation index, and for fault pattern recognition, the vectors as samples, which are composed of features extracted via EWNAP-OLPP, are fed into the back propagation (BP) neural network. The analysis on a fault case of rolling bearings shows that the proposed method is robust for vibro-acoustic fault diagnosis of rolling bearings under various operating conditions and superior to traditional methods.

Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections (OLPP). Finally, gene classification is done by an OLSTM classifier. Here the traditional long short term memory (LSTM) is modified using parameter optimization which uses the adaptive artificial flora optimization (AAFO) algorithm. Based on the migration and flora reproduction process, the AAFO algorithm is stimulated. Using the accuracy, sensitivity, specificity, precision, recall, and f-measure, the proposed performance is analyzed. The test outcomes illustrate the effectiveness of the gene expression data classification with a 94.19% of accuracy value. The proposed gene expression data classification is implemented in the MATLAB platform.

An Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Images

Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) and orthogonal locality preserving projection (OLPP) are two known linear manifold learning algorithms. In this study, scatter information of a distance matrix is used to construct a weight matrix with a supervised approach for the LPP and OLPP algorithms to improve classification accuracy rates. Low-dimensional data are classified with SVM and the results of the proposed method are compared with some other important existing linear manifold learning methods. Class-based enhancements and coefficients proposed for the formulization are reported visually. Furthermore, the change on weight matrices, band information, and correlation matrices with p-values are extracted and visualized to understand the effect of the proposed method. Experiments are conducted on hyperspectral imaging (HSI) with two different datasets. According to the experimental results, application of the proposed method with the LPP or OLPP algorithms outperformed traditional LPP, OLPP, neighborhood preserving embedding (NPE) and orthogonal neighborhood preserving embedding (ONPE) algorithms. Furthermore, the analytical findings on visualizations show consistency with obtained classification accuracy enhancements.

Robust and accurate prediction of protein\u2013protein interactions by exploiting evolutionary information

Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

Ant Lion Optimization Based Medical Data Classification Using Modified Neuro Fuzzy Classifier

The progression of converting depiction of medical analysis and measures into widespread medical code numbers is known as medical classification. According to the conclusion of medical investigation or description of medical handling, the classification of difficulty is resolved by means of the medical specialist in medical region. Primarily preprocessing is employed to alter appropriate model from unprocessed medical datasets in obtainable medical data classification process. After that, orthogonal local preserving projection (OLPP) is exploited to diminish the great dimensions of attribute. At last, the combination of artificial bee colony algorithm with neural network is used for classification of disease. This progression is extra time overriding and encompass poor exactness because classification is carry out devoid of removing the significant attribute from the medical dataset. Therefore, the proposed method is employed to eliminate of these difficulty. At this point the preprocessing is carrying out the medical dataset. It aids to eliminate the unnecessary noises and deficiency take place in the unprocessed medical datasets. Afterward feature choice is carry out. In medical data classification, feature choice is as well recognized as variable choice, attributes choice or variable subset choice which is the progression of choosing a subset of related attribute (variables, predictors). The ant lion optimization (ALO) is utilized for this intention. After that, classification is carrying out for distinguishing the occurrence and nonappearance of diseases. The intention is bring about by means of Modified Neuro Fuzzy classifier. The regulations engender through this classifier which are optimized by means of fruit fly optimization algorithm (FFOA). Therefore the medical data can be categorized into normal or abnormal one. This process task is quicker and generates extra precise consequences than the obtainable process. The presentation of the proposed process is calculated in expressions of accuracy, specificity and sensitivity value. The proposed process is executed in MATLAB.

Hybrid mechanism for medical data classification

In recent years, medical data mining is the hot topic of research. Earlier extraction was done manually so the process was time consuming. Thus, to solve this drawback machine learning technique was implemented to extract knowledge from the large dataset. In this paper, the medical data processed for removal of missing values, then orthogonal local preserving projection (OLPP) algorithm is employed for feature dimension reduction. Then, fuzzy rules are generated and optimised by binary cuckoo search algorithm. Further, the classification is carried out using the generated fuzzy rules and the best rule is selected based on the accuracy using the decision tree classifier. The performance of the proposed technique has been evaluated using University of California Irvine (UCI) datasets namely Cleveland, Hungarian, Mammographic masses, PID and Switzerland. After observation and comparison, the method achieves highest accuracy of 97.3% for PID dataset whereas the existing method provides 89.5% accuracy for PID dataset.

A facial expression recognition model using hybrid feature selection and support vector machines

Facial expression recognition is a challenging issue in the field of computer vision. Due to the limited feature extraction capability of a single feature descriptor, in this paper, a hybrid feature extraction is utilised. The proposed methodology includes local and global feature extractions that is done by local binary pattern (LBP) and histogram orientation gradient (HOG) respectively. Before applying the feature extraction process, pre-processing and face detection is applied on the face image to extract the useful features. The Viola and Jones algorithm is utilised for face detection and the hybrid Laplacian of Gaussian (HLOG) is used for pre-processing stage. The orthogonal local preserving projection (OLPP)-based dimension reduction algorithm is applied to the extracted features to minimise the computational complexity of the classification algorithm. The SVM classification algorithm is utilised for identifying the facial expression. Here, standard CK+ facial expression dataset is used for evaluating the proposed methodology. The proposed methodology performed well in terms of accuracy compared to the existing PCA + Gabor and PCA + LBP methodology.

Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP

Schizophrenia is a severe brain disease that influences the behaviour and thought of person. These effects may fail in achieving the expected levels of interpersonal, academic, or occupational functioning. Although the underlying mechanism is not yet clear, the early detection of schizophrenia is an attractive and challenging research area. There are differences in brain connections of patients and healthy people. This study presents a new computer-aided diagnosis (CAD) method to diagnose schizophrenia (SZ) patients from normal control (NC) people by using the rest-state functional magnetic resonance imaging (R-fMRI) data. fMRI data has a huge dimension, and extracting efficient features is still an open challenge for a schizophrenia diagnosis. In the proposed method, at first orthogonal locality preserving projection (OLPP) is used to reduce the number of time points in R-fMRI scans. Then, an independent component analysis (ICA) algorithm is employed to estimate the independent components (ICs). Next, orthogonal Ripplet-II transform is applied to each IC to extract features. Afterward, a two-sample T-test is implemented on the extracted features to find the most discriminative features. Then, the number of selected features is reduced by applying OLPP. Finally, a test subject is classified into SZ or NC using a linear support vector machine (SVM) classifier. The proposed method is evaluated on the NAMIC and COBRE databases. The results demonstrate that the introduced method significantly outperforms previously presented methods.

A Novel Approach for Pose Invariant Face Recognition in Surveillance Videos

The face detection and face recognition methods are introduced to confirm the abnormal human activity in the video surveillance system. Face detection is carried out by Viola-Jones face detector. It is composed of three concepts namely integral image, AdaBoost and the cascade structure. After face detection, Histogram of Oriented Gradient(HOG) and Weighted Local Binary Patterns(WLBP) features are extracted and those are used in Orthogonal Locality Preserving Projection(OLPP) for face recognition. The detected faces may contain pose variation which dramatically degrades the OLPP based face recognition. So, pose-invariant OLLP-based face recognition is proposed where Histogram of Face Orientation(HFO) and Histogram of Face Direction(HFD), HOG and WLBP features are used in OLPP for efficient face recognition.

A Facial Expression Recognition Model using Hybrid Feature Selection and Support Vector Machines

Facial expression recognition is a challenging issue in the field of computer vision. Due to the limited feature extraction capability of a single feature descriptor, in this paper, a hybrid feature extraction is utilised. The proposed methodology includes local and global feature extractions that is done by local binary pattern (LBP) and histogram orientation gradient (HOG) respectively. Before applying the feature extraction process, pre-processing and face detection is applied on the face image to extract the useful features. The Viola and Jones algorithm is utilised for face detection and the hybrid Laplacian of Gaussian (HLOG) is used for pre-processing stage. The orthogonal local preserving projection (OLPP)-based dimension reduction algorithm is applied to the extracted features to minimise the computational complexity of the classification algorithm. The SVM classification algorithm is utilised for identifying the facial expression. Here, standard CK+ facial expression dataset is used for evaluating the proposed methodology. The proposed methodology performed well in terms of accuracy compared to the existing PCA + Gabor and PCA + LBP methodology.

Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rm SPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.

Improved artificial neural network with aid of artificial bee colony for medical data classification

The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.

A novel approach for diagnosing heart disease with hybrid classifier

A Cardiovascular disease which is additionally referred to as heart diseases that have been a common and steady issue in the field of medical research. Nowadays various methods were applied which is not robust for the prediction of human being expenses and disease risks for patients. This paper proposed an Orthogonal Local Preserving Projection (OLPP) method to reduce the function dimension of the input high-dimensional data. The dimension reduction improves the prediction rate with the help of hybrid classifier i.e. Group Search Optimization Algorithm (GSO) combine with the Levenberg-Marquardt (LM) training algorithm in the neural network. The LM training algorithm is used to solve the optimization problem and it determines the best network parameters such as weights and bias that minimizes the error. The final output of the optimization technique is combined with the performance metrics as accuracy, sensitivity, and specificity. From the result, it is observed that hybrid optimization techniques increase the accuracy of the heart disease prediction system.

Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction.

The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive. Therefore, in this paper, we propose a fast and orthogonal version of LPP, called FOLPP, which simultaneously minimizes the locality and maximizes the globality under the orthogonal constraint. As a result, the computation burden of the proposed algorithm can be effectively alleviated compared with the OLPP algorithm. Experimental results on two face recognition data sets and two hyperspectral data sets are presented to demonstrate the effectiveness of the proposed algorithm.

Improved Artificial Neural Network (ANN) With Aid of Artificial Bee Colony (ABC) For Medical Data Classification

The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.

Optimal fuzzy min-max neural network for medical data classification using group search optimiser algorithm

Several techniques were applied to healthcare datasets for the prediction of future healthcare utilisation such as predicting individual expenditures and disease risks for patients. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied to extract useful data and to convert suitable samples from raw medical datasets. Feature dimension reduction method will be applied to reduce the features' space without losing the accuracy of prediction. Here, orthogonal local preserving projection (OLPP) will be used. Once the feature reduction is formed, the prediction will be carried out based on the optimal classifier. In the optimal classifier, group search optimiser algorithm will be used for fuzzy min-max neural network. Here, the experimentation is done by using various datasets from UCI machine learning repository.

Optimal fuzzy min-max neural network for medical data classification using group search optimiser algorithm

Several techniques were applied to healthcare datasets for the prediction of future healthcare utilisation such as predicting individual expenditures and disease risks for patients. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied to extract useful data and to convert suitable samples from raw medical datasets. Feature dimension reduction method will be applied to reduce the features' space without losing the accuracy of prediction. Here, orthogonal local preserving projection (OLPP) will be used. Once the feature reduction is formed, the prediction will be carried out based on the optimal classifier. In the optimal classifier, group search optimiser algorithm will be used for fuzzy min-max neural network. Here, the experimentation is done by using various datasets from UCI machine learning repository.

BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL

Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.

Multi-Criteria in Discriminant Analysis to Find the Dominant Features

A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra. Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP).

2D Orthogonal Locality Preserving Projection for Image Denoising.

Sparse representations using transform-domain techniques are widely used for better interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a linear technique that tries to preserve local structure of data in the transform domain as well. Vectorized nature of OLPP requires high-dimensional data to be converted to vector format, hence may lose spatial neighborhood information of raw data. On the other hand, processing 2D data directly, not only preserves spatial information, but also improves the computational efficiency considerably. The 2D OLPP is expected to learn the transformation from 2D data itself. This paper derives mathematical foundation for 2D OLPP. The proposed technique is used for image denoising task. Recent state-of-the-art approaches for image denoising work on two major hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data. Locality preserving nature of the proposed approach automatically takes care of self-similarity present in the image while inferring sparse basis. A global basis is adequate for the entire image. The proposed approach outperforms several state-of-the-art image denoising approaches for gray-scale, color, and texture images.