Using uncorrelated discriminant analysis for tissue classification with gene expression data

Uncorrelated trace ratio linear discriminant analysis for undersampled problems

Chapter 9 - Effective dimensionality reduction model with machine learning classification for microarray gene expression data

This paper presents a novel and interesting combination of uncorrelated linear discriminant analysis and wavelets to extract features for face recognition. The proposed algorithm when compared with conventional Fisherface method and ULDA+PCA method has an improved recognition rate and a decrease of computational load for face images with high resolutions. In the proposed technique, the face images are divided into smaller sub-images by 2-D DWT and the uncorrelated linear discriminant analysis is applied to approximations sub-images. The time-cost of the proposed method is greatly reduced and recognition rates ranging between 95% and 97.5% are obtained on the ORL database. An average error rate of 1.4% is obtained with the experiments on the NUST603 database. In addition, the effect of number of discriminant vectors on the recognition system is systematically discussed.

Analysis of EMG signal has been an interested topic in recent years for classifying surface myoelectric signal patterns. Myoelectric control is an unconventional method to control the upper limb prostheses, human-assisting robots and rehabilitation devices. The aim of present work is to assess the time-domain features of EMG signal for myoelectric control of upper extremity prostheses by utilizing scatter plot. Classification accuracy is calculated using linear discriminant classifier for different combination of feature vectors using principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques. Results show that willison amplitude and waveform length are the best features for separating the distinct upper-limb motions.

Linear Discriminant Analysis (LDA) is one of the well-known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.

Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data

Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction and dimension reduction. The performance of classical LDA however, significantly degrades on the High Dimension Low Sample Size (HDLSS) data for the ill-posed inverse problem. Existing approaches for HDLSS data classification typically assume the data in question are with Gaussian distribution and deal the HDLSS classification problem with regularization. However, these assumptions are too strict to hold in many emerging real-life applications, such as enabling personalized predictive analysis using Electronic Health Records (EHRs) data collected from an extremely limited number of patients who have been diagnosed with or without the target disease for prediction. In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier. To fill the gap, in this paper, we first studied an analytical model that understands the accuracy of LDA for classifying data with arbitrary distribution. The model gives a theoretical upper bound of LDA error rate that is controlled by two factors: (1) the statistical convergence rate of (inverse) covariance matrix estimators and (2) the divergence of the training/testing datasets to fitted distributions. To this end, we could lower the error rate by balancing the two factors for better classification performance. Hereby, we further proposed a novel LDA classifier De-Sparse that leverages De-sparsified Graphical Lasso to improve the estimation of LDA, which outperforms state-of-the-art LDA approaches developed for HDLSS data. Such advances and effectiveness are further demonstrated by both theoretical analysis and extensive experiments on EHR datasets https://www.overleaf.com/project/5d2728c718f6ff3b2bcf5991 .

A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a finer and more informative classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearest-neighbor classifiers, linear discriminant analysis, and classification trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high-performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least-squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.

Linear Discriminant Analysis (LDA) is a popular statistical method for both feature extraction and dimensionality reduction in face recognition. The major drawback of LDA is the so-called small sample size (3S) problem. This problem always occurs when the total number of training samples is smaller than the dimension of feature space. Under this situation, the within-class scatter matrix Sw becomes singular and LDA approach cannot be implemented directly. To overcome the 3S problem, this paper proposes a novel wavelet-face based subspace LDA algorithm. Wavelet-face feature extraction and dimensionality reduction are based on two-level D4-filter wavelet transform and discarding the null space of total class scatter matrix St. It is shown that our obtained projection matrix satisfies the uncorrelated constraint conditions. Hence in the sense of statistical uncorrelation, this projection matrix is optimal. The proposed method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. Comparing with existing LDA-based methods to solve the 3S problem, our method gives the best performance.

Deoxyribonucleic acid (DNA) microarrays are part of a promising class of biotechnologies that allow the simultaneous monitoring of expression levels in cells for thousands of genes. One of important issues in microarray experiments is the classification of biological samples and predicting clinical or other outcomes using gene expression data. A closely related issue is the identification of marker genes that have good predictive power for an outcome of interest. Although classification is not a new subject in the statistical literature, the large number of genes with relatively small sample size generated by microarray experiments raises new computational challenges. In this study, the gene expressions of breast cancer tumors are investigated and the performance of several popular classification methods, including decision tree, logistic regression, linear discriminant analysis, and k-nearest neighbor are compared. The results show that certain genes are significantly differentially expressed across groups of patients, and k-nearest neighbor method achieves better performance in class prediction than the other classification methods.

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. Matlab scripts are available on request.

Discrimination of type 2 diabetes mellitus corresponding to different traditional Chinese medicine syndromes based on plasma fatty acid profiles and chemometric methods

This paper proposes a new classification method based on Dirichlet process mixtures(DPM) to investigate the classification of the four actions from the action surface EMG(ASEMG) signals. This method first builds a classification model of the data by using the multinomial logit model (MNL). Then a classifier is given by using the classification information of training data. For the features of ASEMG, we use a combined method of the empirical mode decomposition(EMD), Largest Lyapunov exponent and Linear discriminant analysis(LDA) dimension reduction. The highest average classification accuracy rates are over 90%. The results indicate that this classification method could be applied the classification of the ASEMG signals.KeywordsDirichlet process mixturethe action Surface EMG(ASEMG) signalempirical mode decomposition(EMD)Largest Lyapunov exponentLinear discriminant analysis(LDA)

Employing fisher discriminant analysis for Arabic text classification