Uncorrelated Discriminant Analysis Research Articles

Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy

Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy

Qualitative Analysis of Lambda-Cyhalothrin on Chinese Cabbage Using Mid-Infrared Spectroscopy Combined with Fuzzy Feature Extraction Algorithms

Excess pesticide residues on cabbage are harmful to humans. In this study, we propose an innovative strategy for a quick and nondestructive qualitative test of lambda-cyhalothrin residues on Chinese cabbage. Spectral profiles of Chinese cabbage leaf samples with different concentrations of surface residues of lambda-cyhalothrin were collected with an Agilent Cary 630 FTIR Spectrometer. Standard normal variate (SNV), multiplicative scatter correlation (MSC), and principle component analysis (PCA) were utilized to preprocess the spectra. Then, fuzzy Foley-Sammon transformation (FFST), fuzzy linear discriminant analysis (FLDA), and fuzzy uncorrelated discriminant transformation (FUDT) were employed to extract features from the spectra data. Finally, k-nearest neighbor (kNN) was applied to classify samples according to the concentration of lambda-cyhalothrin residue. The highest identification accuracy rates of FFST, FLDA, and FUDT were 100%, 97.22%, and 100%, respectively. FUDT performed the best considering the combination of accuracy rate and required computing time. We believe that mid-infrared spectroscopy combined with fuzzy uncorrelated discriminant analysis is an effective method to accurately and quickly conduct qualitative analyses of lambda-cyhalothrin residues on Chinese cabbages. This method may have applications in other crops and other pesticide residues.

Tensor-based Uncorrelated Multilinear Discriminant Analysis for Epileptic Seizure Prediction.

Epileptic seizure prediction explores the probability of forecasting the onset of epileptic seizure, which aids to timely treatment for patients. It provides a time lead compared to traditional seizure detection. In this paper, a spectral feature extraction is developed and the seizure prediction is performed based on uncorrelated multilinear discriminant analysis (UMLDA) and Support Vector Machine (SVM). To make best use of information in different dimension, we construct a three-order tensor in temporal, spectral and spatial domain by wavelet transform. And UMLDA implements the tensor-to-vector projection (TVP) with the minimum redundancy. The proposed solution employed 23 subjects' Electroencephalogram (EEG) data from Boston Children's Hospital-MIT scalp EEG dataset, each subject contains 40 minutes EEG signal. For the classification task of ictal state and preictal state, it exhibits an overall accuracy of 95%.

Multi-view uncorrelated discriminant analysis via dependence maximization

This paper proposes a novel multi-view discriminant analysis based on Hilbert-Schmidt Independence Criterion (HSIC) and canonical correlation analysis (CCA). We use HSIC to identify a lower dimensional discriminant common subspace in which the dependence between multi-view features and the associated labels is maximized. CCA is utilized to achieve maximum correlation between different views in the common subspace. Motivated by the successful application of uncorrelated discriminant analysis, we further extend our approach to extract features with minimum redundancy. Experimental results validate the effectiveness of our proposed approaches.

Multiview Uncorrelated Discriminant Analysis.

Multiview learning is more robust than single-view learning in many real applications. Canonical correlation analysis (CCA) is a popular technique to utilize information stemming from multiple feature sets. However, it does not exploit label information effectively. Later multiview linear discriminant analysis (MLDA) was proposed through combining CCA and linear discriminant analysis (LDA). Due to the successful application of uncorrelated LDA (ULDA), which seeks optimal discriminant features with minimum redundancy, we propose a new supervised learning method called multiview ULDA (MULDA) in this paper. This method combines the theory of ULDA with CCA. Then we adapt discriminant CCA (DCCA) instead of the CCA in MLDA and MULDA, and discuss about the effect of this modification. Furthermore, we generalize these methods to the nonlinear case by kernel-based learning techniques. The new method is called kernel multiview uncorrelated discriminant analysis (KMUDA). Then we modify kernel multiview discriminant analysis and KMUDA by replacing Kernel CCA with Kernel DCCA. Our methods are tested on different real datasets and compared with other state-of-the-art methods. Experimental results validate the effectiveness of our methods.

QUANTUM PARTICLE SWARM OPTIMIZATION CLASSIFICATION ALGORITHM AND ITS APPLICATIONS

Data processing in high-dimensional spaces is a challenging task. In order to effectively classify the data in a high-dimensional space, a quantum particle swarm optimization classification algorithm (QPSOCA) for high-dimensional datasets is proposed in this paper. In QPSOCA, an uncorrelated discriminant analysis algorithm is utilized to reduce the dimension of the data, which is implemented automatically and no extra parameters are needed. In addition, to avoid the randomness of the swarm and improve the convergence speed, quantum computation is introduced into particle swarm optimization (PSO). In the experimental section, a detailed comparison of three different combinatorial optimization methods is given to demonstrate the efficiency of the proposed algorithm. Comparative experiments show that the proposed algorithm can improve the classification accuracy.

Two-dimensional color uncorrelated discriminant analysis for face recognition

This paper presents a novel color face recognition method called two-dimensional color uncorrelated discriminant analysis (2DCUDA), which can extract two-dimensional color uncorrelated features and simultaneously retain the face spatial structure information. The 2DCUDA method seeks to explore color uncorrelated discriminant properties of the color face images and eliminate the correlations between color-based features. The novelties of this paper are twofold. First, this paper develops a new color-based feature for face recognition, which can provide substantial mutual complementation information and improve the recognition performance. Second, theoretical analysis guarantees the uncorrelated property of the obtained color-based features. Comparative experiments on AR and FRGC-2 color face databases have been conducted to investigate the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm performs better than other color face recognition methods and the two-dimensional color uncorrelated discriminant features are more effective for low-resolution image compared with conventional gray-based features. Finally, we explain why the proposed algorithm can improve the recognition performance compared with other color face recognition methods.

Supervised immune clonal evolutionary classification algorithm for high-dimensional data

Classification is very difficult in high-dimensional spaces because learning methods suffer from the curse of dimensionality. In order to efficiently classify the high-dimensional data, a Supervised Immune Clonal Evolutionary Classification Algorithm (SICECA) is proposed in this paper. First, the automatic nonparametric Uncorrelated Discriminant Analysis (UDA) is adopted for Dimensionality Reduction (DR), which combines rank-preserving dimensionality reduction with constraint discriminant analysis so as to realize the extracted features statistically uncorrelated. Then, an Immune Clonal Evolutionary Algorithm (ICEA) based on clonal selection principle in immunology is proposed to act as classifier. In the experiments, first of all, 11 UCI data sets, four texture images and three Synthetic Aperture Radar (SAR) images are used to test the performance of SICECA. SICECA is also compared with three existing algorithms in terms of classification accuracy and running time. In addition, SICECA is applied to a real world application, namely, face recognition, with a good performance obtained.

Extracting Features from Optimizsation Techniques to Discover Liability of Customers Using Decision Trees

Most algorithms based on data mining are used to discover customer models for distributing information, which is used in Customer Relationship Management (CRM), for pointing out customers who are loyal and who are attritors, but human expertise is a must for discovering knowledge manually. Many post processing techniques have been introduced that do not suggest action to increase the objective function such as profit. In this paper, a feature extraction technique is proposed for the best approximation property. An Uncorrelated Discriminant Analysis (UDA) algorithm based on Maximum Margin Criterion (MMC) is used. The extracted features via UDA are statistically uncorrelated. It serves as an effective solution for small sample size problem. The extracted features are given as an input to a novel algorithm that suggests actions to change the customer from the undesired status to the desired one. These algorithms can discover the reduction in cost and transform customer from undesirable classes to desirable ones. The UDA algorithm is evaluated in terms of classification accuracy and robustness. Many tests have been conducted and experimental results have been analyzed in this paper.

Uncorrelated discriminant simplex analysis for view-invariant gait signal computing

Human gait is a useful biometric signature and has recently gained growing interest from computer vision researchers. This interest is strongly driven by the need for automatic human identification and gender recognition at a distance in many surveillance applications. Existing human gait analysis methods, however, are sensitive to the view of the gait sequences, and their performances are poor when the view of the training gait sequences is different from that of the testing ones. In this paper, we propose a new supervised manifold learning algorithm, called uncorrelated discriminant simplex analysis (UDSA), for view-invariant gait signal computing. The aim of UDSA is to seek a mapping to project human gait sequences collected from different views into a low-dimensional feature subspace, such that intraclass geometrical structures are preserved and interclass distances of gait sequences are maximized simultaneously. Moreover, we impose an uncorrelated constraint to make the extracted features statistically uncorrelated. Experimental results are presented to demonstrate the efficacy of the proposed approach.

Kernel Uncorrelated and Regularized Discriminant Analysis: A Theoretical and Computational Study

Linear and kernel discriminant analyses are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analyses have been proposed to overcome the singularity problem encountered by classical discriminant analysis. In this paper, we study the properties of kernel uncorrelated and regularized discriminant analyses, called KUDA and KRDA, respectively. In particular, we show that under a mild condition, both linear and kernel uncorrelated discriminant analysis project samples in the same class to a common vector in the dimensionality-reduced space. This implies that uncorrelated discriminant analysis may suffer from the overfitting problem if there are a large number of samples in each class. We show that as the regularization parameter in KRDA tends to zero, KRDA approaches KUDA. This shows that KUDA is a special case of KRDA and that regularization can be applied to overcome the overfitting problem in uncorrelated discriminant analysis. As the performance of KRDA depends on the value of the regularization parameter, we show that the matrix computations involved in KRDA can be simplified, so that a large number of candidate values can be cross-validated efficiently. Finally, we conduct experiments to evaluate the proposed theories and algorithms.

Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data

High-dimensional data and the small sample size problem occur in many modern pattern classification applications such as face recognition and gene expression data analysis. To deal with such data, one important step is dimensionality reduction. Principal component analysis (PCA) and between-group analysis (BGA) are two commonly used methods, and various extensions of these two methods exist. The principle of these two approaches comes from their best approximation property. From a pattern recognition perspective, we show that PCA, which is based on the total scatter matrix, preserves linear separability, and BGA, which is based on between-class scatter matrix, retains the distance between class centroids. Moreover, we propose an automatic nonparameter uncorrelated discriminant analysis (UDA) algorithm based on the maximum margin criterion (MMC). The extracted features via UDA are statistically uncorrelated. UDA combines rank-preserving dimensionality reduction and constraint discriminant analysis and also serves as an effective solution for the small-sample-size problem. Experiments with face images and gene expression data sets are conducted to evaluate UDA in terms of classification accuracy and robustness.

A Direct Kernel Uncorrelated Discriminant Analysis Algorithm

In this letter, we present a new formulation for uncorrelated discriminant analysis (UDA) in some high-dimensional feature space and then propose an efficient UDA algorithm using kernel technique. Unlike some existing UDA algorithms, which solve uncorrelated discriminant vectors one at a time, the proposed algorithm is able to extract all the uncorrelated discriminant vectors simultaneously in the feature space and does not suffer the small sample size problem. Experimental results show that the proposed method is very competitive in comparison with some existing discriminant analysis algorithms, in terms of recognition rate and robustness with respect to kernel parameters.

Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction

We propose a generalized null space uncorrelated Fisher discriminant analysis (GNUFDA) technique integrating the uncorrelated discriminant analysis and weighted pairwise Fisher criterion. The GNUFDA can effectively deal with the small sample-size problem and perform satisfactorily when the dimensionality of the null space decreases with increase in the number of training samples per class and/or classes, C. The proposed GNUFDA can extract at most C - 1 optimal uncorrelated discriminative vectors without being influenced by the null-space dimensionality.

Kernel Uncorrelated Discriminant Analysis and Its Application to Handwritten Character Recognition

Kernel Uncorrelated Discriminant Analysis and Its Application to Handwritten Character Recognition

A note on kernel uncorrelated discriminant analysis

In this paper, we give a theoretical analysis on kernel uncorrelated discriminant analysis (KUDA) and point out the drawbacks underlying the current KUDA algorithm which was recently introduced by Liang and Shi [Pattern Recognition 38(2) (2005) 307–310]. Then we propose an effective algorithm to overcome these drawbacks. The effectiveness of the proposed method was confirmed by experiments.

Using uncorrelated discriminant analysis for tissue classification with gene expression data

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant Analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant Analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.