Subspace Discriminant Analysis Research Articles

Fuzzy Recurrence Plots for Shallow Learning-Based Blockage Detection in a Centrifugal Pump Using Pre-Trained Image Recognition Models

Abstract Rags, dusts, foreign particles, etc., are the primary cause of blockage in the centrifugal pump and deteriorate the performance. This study elaborates an experimental and data-driven methodology to identify suction, discharge, and simultaneous occurrence of both blockages. The discharge pressure signals are acquired and denoised using CEEMD. The fuzzy recurrence plots obtained from denoised signals are attempted to classify using three pre-trained models: Xception, GoogleNet, and Inception. None of these models are trained on such images; thus, features are extracted from different pooling layers which include shallow features too. The features extracted from different layers are fed to four shallow learning classifiers: Quadratic SVM, Weighted k-nearest network, Narrow Neural network, and subspace discriminant classifier. The study finds that subspace discriminant achieves the highest accuracy of 97.8% when trained using features from second pooling of Xception model. Furthermore, this proposed methodology is implemented at other blockage conditions of the pump. The subspace discriminant analysis outperforms the other selected shallow classifier with an accuracy of 93% for the features extracted from the first pooling layer of the Xception model. Therefore, this study demonstrates an efficient method to identify pump blockage using pre-trained and shallow classifiers.

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis

Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property.

Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture

Effective recognition of fruit leaf diseases has a substantial impact on agro-based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked-eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real-time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre-trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach-based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.

Novel dynamic enhanced robust principal subspace discriminant analysis for high-dimensional process fault diagnosis with industrial applications

Since the data are often polluted by numerous measured noise or outliers, traditional subspace discriminant analysis is difficult to extract optimal diagnostic information. To alleviate the impact of the problem, a robust principal subspace discriminant analysis algorithm for fault diagnosis is designed. On the premise of decreasing the impact of redundant information, the optimal latent features can be calculated. Specifically, in the algorithm, dual constraints of the weighted principal subspace center and l2,1-norm are introduced into the objective function to suppress outliers and noise. Besides, considering that the current changes of the data in a dynamic process rely on past observations, merely analyzing the current data may lead to an incorrect interpretation of the mechanism model, especially in the presence of similar variable data under the two different conditions. Therefore, based on the robust principal subspace discriminant analysis, we further develop its dynamic enhanced version. The dynamic enhanced method utilizes the dynamic augmented matrix to enhance the latent features of historical data into current shifted features, so as to enlarge the difference between similar modes. Finally, the experimental results arranged on the Tennessee Eastman process and a commercial multi-phase flow process demonstrate that the proposed method has advanced diagnostic performance and satisfactory convergence speed.

Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning.

A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection

With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.

Spectral feature extraction of hyperspectral remote sensing images based on class pair-weighted criterion

Hyperspectral remote sensing images (HRSIs) face the problem of extracting efficient discriminant features when foreign objects with similar spectra and outlier classes exist. For solving the problem, first, we derive a class pair (CP) form of the classical Fisher criterion, namely CP Fisher criterion, where the between-class scatter matrix and within-class scatter matrix are both decomposed into the form of CPs. And then, a CP-weighted criterion is proposed to weight the between-class scatter matrix and within-class scatter matrix of each CP according to its separability so that the separabilities of CPs are balanced in the CP-weighted feature subspace. The Bayesian classifier and k-nearest neighbors classifier are used to evaluate the feature extraction performance. Experimental results on three real HRSIs show that the presented CP-weighted subspace method improves the classification accuracies of the CPs with small separabilities so that it is superior to the linear discriminant analysis subspace method and the weighted pairwise Fisher subspace method.

A robust geometric mean-based subspace discriminant analysis feature extraction approach for image set classification

Discriminant analysis technique is an important research topic in image set classification because it can extract discriminative features. However, most existing discriminant analysis methods almost fail to work for feature extraction of data because there is only a small amount of valid discriminant information. The main weakness of most existing discriminant analysis models is that the class mean vector is constructed by using class sample average. That is not sufficient to provide an accurate estimation of the class mean. In this paper, we proposed a robust geometric mean-based subspace discriminant analysis feature extraction method for image set classification. This method is a combination of geometric mean vector, subspace and covariance matrix to jointly represent an image set because they are contain different discriminative information. The above three representation ways lie on different spaces. To reduce the dissimilarity between the heterogeneous spaces, a robust geometric mean-based subspace discriminant analysis learning (GMSDA) framework is developed, which includes three steps operation. At first, uses a new geometric mean vector to construct geometric between-class scatter matrix (Sgb) and geometric within-class scatter matrix (Sgw) instead of traditional mean vector of many methods. Secondly, maximizes the geometric between-class scatter matrix to increase the difference between extracted features. Finally, maximizes the subspace between-class scatter (SgbS) and minimizes the subspace within-class scatter (SgwS) simultaneously in the subspace of original space. Experiments on five datasets illustrate that our proposed GMSDA method works the best in small sample size situation by using maximum likelihood classification (MLC).

Cross domain association using transfer subspace learning

Transfer learning has gained more attention recently by utilizing knowledge acquired from one domain to advance a learning performance in another domain. Existing homogeneous transfer learning methods have progressed to a point where feature spaces are common in training and testing domains. However, heterogeneous transfer learning is still in its nascent stage where features of training and testing domains are different. Taking this into account, Bregman Divergence Regularization is used to minimize the probability distribution difference between training and testing domains and to take them together to a shared subspace. To discriminate data within individual domains, a projection matrix is obtained using Fisher Linear Discriminant Analysis subspace learning algorithm. Experimentation is performed on two efficiently used biometrics: the face and fingerprint. Two types of cross-domain settings are used: (1) Face + Finger2Finger where training samples come from face (labeled samples) and fingerprint (unlabeled samples) data sets, while testing is performed on a fingerprint dataset. (2) Finger + Face2Face where training samples come from fingerprint (labeled samples) and face (unlabeled samples) data sets while testing is performed on a face dataset. This paper proposes a cross domain association between face and fingerprint that finds utility in forensic applications.

A novel discriminant multiscale representation for ear recognition

This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.

Class‐specific variable selection in high‐dimensional discriminant analysis through Bayesian Sparsity

AbstractAlthough the ongoing digital revolution in fields such as chemometrics, genomics, or personalized medicine gives hope for considerable progress in these areas, it also provides more and more high‐dimensional data to analyze and interpret. A common usual task in those fields is discriminant analysis, which however may suffer from the high dimensionality of the data. The recent advances, through subspace classification or variable selection methods, allowed to reach either excellent classification performances or useful visualizations and interpretations. Obviously, it is of great interest to have both excellent classification accuracies and a meaningful variable selection for interpretation. This work addresses this issue by introducing a subspace discriminant analysis method which performs a class‐specific variable selection through Bayesian sparsity. The resulting classification methodology is called sparse high‐dimensional discriminant analysis (sHDDA). Contrary to most sparse methods which are based on the Lasso, sHDDA relies on a Bayesian modeling of the sparsity pattern and avoids the painstaking and sensitive cross‐validation of the sparsity level. The main features of sHDDA are illustrated on simulated and real‐world data. In particular, an exemplar application to cancer characterization based on medical imaging using radiomic feature extraction is proposed.

Selective Feature Generation Method for Classification of Low-Dimensional Data

We propose a method that generates input features to effectively classify low-dimensional data. To do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the ‘discrimination distance’ for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features.

Application of image recognition in civil aviation security based on tensor learning

Image recognition is a hot topic in the field of computer vision and pattern recognition, it is widely used in identification, automatic control, human-computer interaction systems. With the development of civil aviation, image recognition has become an important tool to ensure civil aviation secur ity. In this article, firstly, tensor is used to represent the image, which can preserve more structure information of image than traditional vector representation. Then, combining a new tensor distance (NTD) and multilinear discriminant subspace analysis (MLDSA), a novel dimensionality reduction approach named NTD-MLDSA is proposed, and the transformation matrices can be obtained by employing an iterative strategy. Different from the Euclidean distance (ED), which bases on orthogonal assumption, NTD takes into account the spatial relationships of elements and can reflect the real distance between tensors. Experimental results show that the propose approach is more appropriate for dimensionality reduction of image objects than other classical dimension reduction methods, based on benchmark recognition databases Yale, ORL and USPS, the low dimensional data obtained by NTD-MLDSA improves the classification accuracy.

Wavelet-Local binary pattern based face recognition

Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

Makeup-invariant face identification and verification using fisher linear discriminant analysis-based Gabor filter bank and histogram of oriented gradients

Non-permanent facial makeup is one of the most difficult problems inhibiting face recognition systems in security applications. In this paper, a new method is proposed for makeup-invariant face identification and verification. Face images from the virtual makeup (VMU) and YouTubemakeup (YMU) datasets were subjected to the Gabor filtering and histogram of oriented gradients (HOG) methods for feature extraction. The Gabor and HOG features were concatenated to generate the final feature vectors and subsequently reduced using the fisher linear discriminant analysis subspace. The reduced features were classified using the city block distance (CBD), Euclidean distance (EUC), cosine similarity measure (CSM) and whitened cosine similarity measure (WCSM). The CSM achieved the best recognition rates out of the four metrics used. Performance evaluation of these metrics produced identification and verification rates of 100% and 100% for the VMU database, and 72.52% and 79.47% for the YMU database, respectively. The developed method outperformed several state-of-the-art methods initially exploited.

Multi-agent hybrid mechanism for financial risk management

Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the corporate financial distress. Design/methodology/approach: This study utilized numerous methods, namely random subspace method, discriminant analysis and decision tree to construct the multi-agent forecasting model. Findings and Originality/value: The study shows a superior forecasting performance. Originality/value: The use of multi-agent model to predict the corporate financial distress.

Noise-Adjusted Subspace Discriminant Analysis for Hyperspectral Imagery Classification

Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for pattern classification; however, its performance is often degraded when samples are too few, particularly when the dimensionality of the input feature space is excessively high. The classic solution to the small-sample-size problem is to implement LDA in a principal component (PC) subspace, i.e., a strategy known as subspace LDA. This latter approach is extended by coupling LDA and noise-adjusted HSI analysis in order to provide noise-robust feature extraction and classification of high-dimensional data. An extension of the proposed approach in a kernel-induced space is also studied. The resulting noise-adjusted subspace discriminant analysis is evaluated using hyperspectral imagery, with experimental results demonstrating that the proposed approach provides not only superior classification performance, as compared with traditional methods, but also effective dimensionality reduction for classification even in the presence of noise.

Fisher Difference Discriminant Analysis: Determining the Effective Discriminant Subspace Dimensions for Face Recognition

In most manifold learning based subspace discriminant analysis algorithms, how to construct the local neighborhood graphs and determine the effective discriminant subspace dimensions in applications are difficult but important problems. In this paper, we propose a novel supervised subspace learning method called Fisher Difference Discriminant Analysis (FDDA) for linear dimensionality reduction. FDDA introduces the local soft scatter to characterize the distributions of the data set. By combining Fisher criterion and difference criterion together, FDDA obtains the optimal discriminant subspace, on which a large margin between different classes is provided for classification. Eigenvalue analysis shows that the effective discriminant subspace dimensions of FDDA can be automatically determined by the number of positive eigenvalues and are robust to noise and invariant to rotations, rescalings and translations of the data. Comprehensive comparison and extensive experiments show that FDDA is superior to some state-of-the-art techniques in face recognition.

A new discriminant subspace analysis approach for multi-class problems

Fukunaga-Koontz Transform (FKT) is a famous feature extraction method in statistical pattern recognition, which aims to find a set of vectors that have the best representative power for one class while the poorest representative power for the other class. Li and Savvides [1] propose a one-against-all strategy to deal with multi-class problems, in which the two-class FKT method can be directly applied to find the presentative vectors of each class. Motivated by the FKT method, in this paper we propose a new discriminant subspace analysis (DSA) method for the multi-class feature extraction problems. To solve DSA, we propose an iterative algorithm for the joint diagonalization (JD) problem. Finally, we generalize the linear DSA method to handle nonlinear feature extraction problems via the kernel trick. To demonstrate the effectiveness of the proposed method for pattern recognition problems, we conduct extensive experiments on real data sets and show that the proposed method outperforms most commonly used feature extraction methods.