Dimensional Linear Discriminant Analysis Research Articles

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms.

Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.

Algorithm Symmetric 2-DLDA for Recognizing Handwritten Capital Letters

Statistical pattern recognition is the process of using statistical techniques to obtain information and make informed decisions based on data measurements. It is possible to solve the doubt inherent in the objective function of the 2-Dimension Linear Discriminant Analysis by employing the symmetrical 2-Dimension Linear Discriminant Analysis approach. Symmetrical 2-dimensional linear discriminant analysis has found widespread use as a method of introducing handwritten capital letters. Symmetric 2-DLDA, according to Symmetric 2-DLDA, produces better and more accurate results than Symmetric 2-DLDA. So far, pattern recognition has been based solely on computer knowledge, with no connection to statistical measurements, such as data variation and Euclidean distance, particularly in symmetrical images. As a result, the aim of this research is to create algorithms for recognizing capital letter patterns in a wide range of handwriting. The ADL2-D symmetric method is used in this study as the development of the ADL2-D method. The research results in an algorithm that considers the left and right sides of the image matrix, as opposed to ADL2-D, which does not consider the left and right sides of the image matrix. In pattern recognition, the results with symmetric ADL2-D are more accurate

Face and Ethnical Group Recognition with Images of Different Resolutions

In this paper, a dataset for Xinjiang minority ethnical groups is introduced, and implementation of two dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Partial Least Squares (2DPLS) is investigated. Two important topics for face recognition and the ethnicity recognition are investigated for database with different image resolutions. Experiments show that 2DLDA performances better than 2DPLS on our face database.

High Dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm and Missing Data

SummaryThe paper develops optimality theory for linear discriminant analysis in the high dimensional setting. A data-driven and tuning-free classification rule, which is based on an adaptive constrained l1-minimization approach, is proposed and analysed. Minimax lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces. In addition, we consider classification with incomplete data under the missingness completely at random model. An adaptive classifier with theoretical guarantees is introduced and the optimal rate of convergence for high dimensional linear discriminant analysis under the missingness completely at random model is established. The technical analysis for the case of missing data is much more challenging than that for complete data. We establish a large deviation result for the generalized sample covariance matrix, which serves as a key technical tool and can be of independent interest. An application to lung cancer and leukaemia studies is also discussed.

A multi-instance multi-sample palmprint identification system

<span>The multibiometric recognition system considered more reliable than the unimodal biometric recognition system due to the addition of an extra information that increases the discrimination between the classes. In this paper, a multi-sample multi-instance biometric recognition system is proposed. The aim of the proposed system is to increase the robustness of the identification. the proposed system also addresses the overfitting to the train samples problem of a feature extraction algorithm, named 2-Dimensional Linear Discriminant analysis (2D-LDA). The samples in the proposed method are bootstrapped and the 2D-LDA performed on each group during the offline phase. While in the online phase, the tested class will be transformed into subspaces using different eigenvectors that obtained from different samplings, and the results matched with templates in the corresponding subspace. To evaluate the proposed method, two palmprint databases are used which are IIT Delhi Touchless Palmprint Database and PolyU palmprint database, and different rank-level fusion algorithms are investigated. The results of the proposed method show improvement in the identification rate.</span>

Sparse L1-norm two dimensional linear discriminant analysis via the generalized elastic net regularization

Linear discriminant analysis (LDA) and two dimensional LDA (2DLDA) are widely applied methods for dimensionality reduction. However, both of them lack of robustness and sparseness. Recent studies show that the elastic net and the L1-norm would improve the learning ability of dimensionality reduction. In this paper, we propose a generalized elastic net and apply it into the L1-norm based LDA and 2DLDA to extend LDA and 2DLDA with robustness and sparseness simultaneously (named LDAL1-S and 2DLDAL1-S, respectively). The generalized elastic net also helps LDAL1-S and 2DLDAL1-S avoiding the singularity problem. Moreover, the Lp-norm (0 < p ≤ 1) is used in the generalized elastic net, which makes LDAL1-S and 2DLDAL1-S realize the desired sparseness of the discriminant vectors by selecting proper p. Both LDAL1-S and 2DLDAL1-S are solved through a series of convex problems with equality constraints, with a closed solution for each single problem. Experimental results on three contaminated human face databases show the effectiveness of the proposed methods.

Structural damage identification using image-based pattern recognition on event-based binary data generated from self-powered sensor networks

A continuing challenge in structural health monitoring is power availability for sensors to collect and communicate data. A way to minimize the communication power demand is to transmit the minimum amount of information, namely, one bit. Event-based binary signals are generated at sensor nodes according to local rules based on physical measurements, but interpretation at the global level requires dealing with discrete binary data, which implies system information with reduced resolution. This study presents an investigation on approaches for the interpretation of event-based binary data provided by a self-powered sensor network using a pulse-communication protocol for use in structural assessment and damage identification. Pattern recognition (PR) methods based on image data analysis techniques were adapted for such purpose. The methods used were classifiers based on anomaly detection, a Bayesian method, and a nearest neighbor method. To improve the performance of the approach, 2-dimensional principal component analysis and 2-dimensional linear discriminant analysis were used as feature extraction techniques along with a nearest neighbor classifier. The PR methods and the performance of the interpretation algorithms were evaluated by using virtual data from finite element simulations and real data from experiments on plates. The ability of the PR methods to identify service demands, load variations, and localized material degradation was examined. Results indicate that image-based PR methods can be effectively used for structural damage identification in plate-like structures using event-based binary data sets in novel wireless self-powered sensor networks.

Classification of facial diagnosis gloss in Chinese medicine based on different algorithms.

To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images. To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)2PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method. All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)2PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%. A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.

Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification

Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through daubechies wavelet transform, dimensionality reduction through linear DR techniques specified, and arrhythmia classification using PNN. Linear dimensionality reduction techniques have simple geometric representations and simple computational properties. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrates that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR technique exhibits highest sensitivity and F score of 78.84% and 78.82% respectively with a minimum of 8 dimensions.

A comparative study of feature extraction methods and their application to P-RBF NNs in face recognition problem

This study is concerned with a design of a face recognition algorithm realized based on feature extraction with the aid of the 2-directional 2-dimensional linear discriminant analysis referred to as (2D)2LDA. The 2DLDA algorithm yields high accuracy, but comes with an unresolved problem of handling large feature matrices present in face recognition problems. The proposed (2D)2LDA algorithm is computationally more efficient and produces more powerful discrimination decision. The proposed P-RBF NNs is used as a recognition module. The architecture of this module consists of three functional components. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results reported for benchmark face datasets – the Yale, ORL database, and IC&CI dataset demonstrate the effectiveness and efficiency of (2D)2LDA algorithm compared with other pre-processing approaches such as LPP, 2D-PCA, 2D-LPP, SR, PCA, (2D)2PCA and fusion of PCA-LDA. The experimental results show that (2D)2LDA based P-RBF NNs achieves higher performance than being reported by other methods.

Two Dimensional Linear Discriminant Analyses for Hyperspectral Data

Most supervised feature extraction methods like linear discriminant analysis (LDA) suffer from the limited number of available training samples. The singularity problem causes LDA to fail in small sample size (SSS) situations. Two dimensional linear discriminant analysis (2DLDA) for feature extraction of hyperspectral images is proposed in this paper which has good efficiency with small training sample size. In this approach, the feature vector of each pixel of hyperspectral image is transformed into a feature matrix. As a result, the data matrices lie in a low-dimensional space. Then, the between-class and within-class scatter matrices are calculated using the matrix form of training samples. The proposed approach has two main advantages: it deals with the SSS problem in hyperspectral data, and also it can extract each number of features (with no limitation) from the original high dimensional data. The proposed method is tested on four widely used hyperspectral datasets. Experimental results confirm that the proposed 2DLDA feature extraction method provides better classification accuracy, with a reasonable computation time, compared to popular supervised feature extraction methods such as generalized discriminant analysis (GDA) and nonparametric weighted feature extraction (NWFE) particularly compared to the 1DLDA in the SSS situation. The experiments show that two dimensional linear discriminant analysis+support vector machine (2DLDA+SVM) is an appropriate choice for feature extraction and classification of hyperspectral images using limited training samples.

Ultrahigh Dimensional Multi-Class Linear Discriminant Analysis by Pairwise Sure Independence Screening

Ultrahigh Dimensional Multi-Class Linear Discriminant Analysis by Pairwise Sure Independence Screening

Dual-kernel based 2D linear discriminant analysis for face recognition

This paper proposes a new image feature extraction method for face recognition, called dual-kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating multiple kernel discriminant analysis with the existing K2DFDA method. The proposed method deals with a face image directly as a matrix, instead of a stacked vector from rows or columns of the image. Moreover, we separately perform an iterative scheme for kernel parameter optimization for each of the two kernels, based on the maximum margin criterion and the damped Newton’s method, followed by a fusion procedure of the two kernels. Experimental results on the ORL and UMIST face databases show the effectiveness of D-K2DLDA.

New Modelling of Modified Two Dimensional Fisherface Based Feature Extraction

Biometric researches have been interesting field for many researches included facial recognition. Crucial process of facial recognition is feature extraction. One Dimensional Linear Discriminant Analysis is one of feature extraction method is development of Principal Component Analysis mostly used by researches. But, it has limitation, it can efficiently work when number of training sets greater or equal than number of dimensions of image training set. This limitation has been overcome by using Two Dimensional Linear Discriminant Analysis. However, search value of matrix identity R and L by using Two Dimensional Linear Discriminant Analysis takes high cost, which is O(n 3 ). In this research, the seeking of “Scatter between Class” and “Scatter within Class” by using Discriminant Analysis without having to find the value of R and L advance are proposed. Time complexity of proposed method is O(n 2 ). Proposed method has been tested by using AT&T face image database. The experimental results show that maximum recognition rate of proposed method is 100%.

Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine

Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA) is used for feature extraction. Support Vector Machine (SVM) is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% . Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR

Face Recognition Systems Using Relevance Weighted Two Dimensional Linear Discriminant Analysis Algorithm

Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.

Weighted Scatter-Difference-Based Two DimensionalDiscriminant Analysis for Face Recognition

Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension. It has been used widely in many applications involving high-dimensional data, such as face recognition, image retrieval, etc. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis (PCA) before LDA. The algorithm, called PCA + LDA, is used widely in face recognition. However, PCA + LDA have high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. Also, Two Dimensional Linear Discriminant Analysis (2DLDA) implicitly overcomes the singular- ity problem, while achieving efficiency. The difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representation of data, while the 2DLDA algorithm works with data in matrix representation. To deal with the singularity problem we propose a new technique coined as the Weighted Scatter-Difference-Based Two Dimensional Discriminant Analysis (WSD2DDA). The algorithm is applied on face recognition and compared with PCA + LDA and 2DLDA. Experiments show that WSD2DDA achieve competitive recognition accuracy, while being much more efficient.

Incremental two-dimensional linear discriminant analysis with applications to face recognition

Two dimensional linear discriminant analysis (2DLDA) has been verified as an effective method to solve the small sample size (SSS) problem in linear discriminant analysis (LDA). However, most of the existing 2DLDA techniques do not support incremental subspace analysis for updating the discriminant eigenspace. Incremental learning has proven to enable efficient training if large amounts of training data have to be processed or if not all data are available in advance as, for example, in on-line situations. Instead of having to re-training across the entire training data whenever a new sample is added, this paper proposed an incremental two-dimensional linear discriminant analysis (I2DLDA) algorithm with closed-form solution to extract facial features of the appearance image on-line. The proposed I2DLDA inherits the advantages of the 2DLDA and the Incremental LDA (ILDA) and overcomes the number of the classes or chunk size limitation in the ILDA because the size of the between-class scatter matrix and the size of the within-class scatter matrix in the I2DLDA are much smaller than the ones in the ILDA. The results on experiments using the ORL and XM2VTS databases show that the I2DLDA is computationally more efficient than the batch 2DLDA and can achieve better recognition results than the ILDA.

Relevance-Weighted (2D)2LDA Image Projection Technique for Face Recognition

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2 - directional and 2-dimensional LDA, or (2D) 2 LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted (2D) 2 LDA, or RW(2D) 2 LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW(2D) 2 LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.