Fisher Linear Discriminant Model Research Articles

Fisher's linear discriminant analysis model for inscribing the effect of chemical composition on cultural heritage

In this study, the relationship between weathering and glass type, decoration and colour was first analysed using Goodman and Kruskal's tau-y coefficients, and a chi-squared test was performed, which showed that weathering was most influenced by glass type, and was influenced by glass decoration but very little by glass colour. A Fisher linear discriminant analysis model was then established, and the eight chemical components with the greatest correlation to artefact type were calculated using SPSS software under the conditions of the two glass types respectively, and optimised using a grey correlation algorithm with specified thresholds for screening components, resulting in four final elements affecting high potassium glass as potassium oxide, magnesium oxide, iron oxide and copper oxide, and affecting lead and barium types as calcium oxide, aluminium oxide, lead oxide and barium oxide, aluminium oxide, lead oxide, barium oxide and strontium oxide.

Research on glass classification based on linear regression model

This paper studies the rule of chemical composition of ancient glass products and the identification of glass types. Through Spearman correlation analysis, the establishment of multiple linear regression model and Fisher linear discriminant model, the relationship and classification rule of chemical composition of different types of glass are determined, and the linear regression model of chemical composition content before and after weathering and the identification method of glass types are obtained. The results show that the mathematical model proposed in this paper can effectively identify glass types and provide guidance for the identification of glass components.

Glass component analysis based on Fisher's linear discriminant model and random forest classification model

The production of ancient glass is the crystallization of the wisdom of the majority of ancient working people in China. The differences in fluxes and the differences in the burial environment during weathering have a great impact on its chemical composition content. In this paper, a relevant classification model is established based on the data of various properties of glass. In this paper, Fisher linear discriminant model and random forest classification model were established according to the content of different chemical components, and Fisher linear discriminant model, which is easier to test for sensitivity analysis, was selected. Next, the clustering results were obtained by systematically clustering high potassium glass and lead-barium glass, and the classification basis was determined by drawing a line graph and then establishing a subclass classification model to classify high potassium glass into low potassium subclasses and high calcium subclasses. Sensitivity analysis was performed by changing the values of key parameters to check the reasonableness and stability of the subclass classification model. A discriminant model was established to initially determine the categories of eight unknown classified glass artifacts. Sensitivity analysis was performed by changing the key variables through the gradient descent method, and the model was obtained to have good sensitivity.

Discovery and Validation of Serum Autoantibodies Against Tumor-Associated Antigens as Biomarkers in Gastric Adenocarcinoma Based on the Focused Protein Arrays.

INTRODUCTION:Previous studies have demonstrated that autoantibodies against tumor-associated antigens (TAAs) in patients with cancer can be used as sensitive immunodiagnostic biomarkers for the detection of cancer. Most of these TAAs are involved in the tumorigenesis pathway. Cancer driver genes with intragenic mutations can promote tumorigenesis. This study aims to identify autoantibodies against TAAs encoded by cancer driver genes in sera as potential immunodiagnostic biomarkers for gastric adenocarcinoma (GAC).METHODS:Protein arrays based on cancer driver genes were customized for screening candidate TAAs in 100 GAC sera and 50 normal control (NC) sera. Autoantibodies against candidate TAAs were assessed by enzyme-linked immunosorbent assay in both training group (205 GAC sera and 205 NC sera) and independent validation group (126 GAC sera and 126 NC sera). Moreover, the immunodiagnostic models were respectively established and validated in the training group and validation group.RESULTS:A panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti–serine/arginine-rich splicing factor 2, and anti-SMARCB1 was selected by the Fisher linear discriminant analysis model with an areas under receiver operating characteristic curve (AUC) of 0.928 (95% confidence interval [CI]: 0.888–0.967) in the training cohort and an AUC of 0.885 (95% CI: 0.852–0.918) in the validation cohort. Besides, the panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-PBRM1, and anti-ACVR1B which were selected by the binary logistic regression model showed an AUC of 0.885 (95% CI: 0.852–0.919) in the training cohort and 0.884 (95% CI: 0.842–0.925) in the validation cohort.DISCUSSION:Two panels which were selected in this study could boost the detection of anti-TAA autoantibodies in sera as biomarkers for the detection of GAC.

Study on Discrimination Model of Water Inrush Source in Binhuang Mining Area of China

Groundwater flow into the mine by coal mining, which threatens the safety of coal mine. Therefore, it is necessary to identify the source of the water flowing into the coal mine. Taking binhuang mining area in Shaanxi Province as the research background, the discrimination models of six water inrush sources had been studied. The composition of 256 water samples was determined and the water quality characteristics of 6 water sources were analyzed. Fisher linear discriminant function model was established by selecting 9 indexes of water quality components. By testing the discriminant effect, it is considered that the probability of K1L aquifer misjudging as Q aquifer and surface water is high, and the misjudgment rate is 30.4%. Combined with the analysis of geological conditions, it is considered that K1L aquifer has a good hydraulic connection with Q aquifer and surface water. Aiming at the problem of misjudgment, the neural network analysis model is used, and the misjudgment rate is reduced to 0%.

Multiple Cytokine Profiling: A New Model to Predict Response to Tumor Necrosis Factor Antagonists in Ulcerative Colitis Patients.

Ulcerative colitis (UC) is a form of inflammatory bowel disease, and antibodies against tumor necrosis factor (anti-TNF) are used for treatment. Many patients are refractory or lose response to anti-TNF, and predicting response would be an extremely valuable clinical tool. Unlike most biomarkers, cytokines directly mediate inflammation, and their measurement may predict the likelihood of response or no response. Serum samples were obtained from 49 UC patients before infliximab infusions, and levels of 17 cytokines were measured using a multiplex assay. The Fisher linear discriminant analysis (FLDA) was applied to the cytokine values to predict which patients would respond to infliximab. "Response" was defined as clinical remission after the third infusion, and "no response" was defined as lack of remission after the third infusion. The Fisher linear discriminant analysis model identified a subset of seven predictor cytokines: TNF-α, IL-12, IL-8, IL-2, IL-5, IL1-β, and IFN-γ. The obtained canonical coefficients enabled to calculate discriminant scores as linear combinations of the cytokines; model classified thepatients as responders and nonresponders with a sensitivity of 84.2% and a specificity of 93.3%. Overall, the yield of the FLDA model was 89.8% of the total 49 patients. An unbiased, statistically derived, predictive model based on measurement of serum cytokines before therapy may predict a positive or negative outcome from the administration of anti-TNF to UC patients. Because accurately measuring cytokines is simple and inexpensive, the model may be a valuable new tool to complement other laboratory parameters used in the management of IBD patients.

Handwritten Devanagari Similar Character Recognition by Fisher Linear Discriminant and Pairwise Classification

The research works in Handwritten Devanagari Characters are continually evolving into new challenges, which exposed the new sources of further research work like, character normalization, gray-level normalization, a discrimination of the similar characters and many more. This paper discusses the discrimination of the similar characters, which is one of the major sources of classification error. The similar shape character has a very minute difference, which is called critical region and used to discriminate them by human beings. The primary goal of the current work is to identify the critical region of the similar character and use the same to generate additional features in order to minimize the classification errors in the end results. It is also quite challenging to identify the critical region as the characters are written in different handwriting styles and fonts. The paper suggestes the Fisher linear discriminant model to detect the critical region, which is used to extract the additional feature. The experiments work was conducted on the standard database, which has 36[Formula: see text]172 handwritten Devanagari characters and significant improvement has been recorded by the aforesaid technique.

Multiple instance learning tracking based on Fisher linear discriminant with incorporated priors

Traditional tracking-by-detection methods use online classifier to track object, and the classifier can be degenerated easily using self-learning process. The article presents a multiple instance learning (MIL) tracking method based on a semi-supervised learning model with Fisher linear discriminant (MILFLD). First, the overlap rate of sampled instances and tracking object served as the prior information. Using both labeled and unlabeled data, the tracking drift problem in the learning model could be alleviated. Second, the lost function of MILFLD is built using Fisher linear discriminant model incorporated with priors. Hence the optimal classifier can be selected out directly in instance level. Last but not least, the classifiers are chosen by gradient descent method, assuring the maximum descent of lost function. Therefore, the classifiers selected at previous frames are still discriminative to future frames, which can help to constrain the error propagation. Comparison experiments show that the center location errors of online AdaBoosting , online MIL tracking, weighted MIL tracking (WMIL), compressive tracking (CT), struck tracking, and MILFLD are 78, 66, 62,74, 59, and 25 pixels, respectively, which demonstrates the tracking accuracy of our method. The experiments of robot motion tracking in realistic scenario have been complemented for comparison as well. Despite the variations in illumination, deformation, or occlusions of the objects, the proposed method can track the target accurately and has high real-time performance.

Identification of double-yolked duck egg using computer vision.

The double-yolked (DY) egg is quite popular in some Asian countries because it is considered as a sign of good luck, however, the double yolk is one of the reasons why these eggs fail to hatch. The usage of automatic methods for identifying DY eggs can increase the efficiency in the poultry industry by decreasing egg loss during incubation or improving sale proceeds. In this study, two methods for DY duck egg identification were developed by using computer vision technology. Transmittance images of DY and single-yolked (SY) duck eggs were acquired by a CCD camera to identify them according to their shape features. The Fisher’s linear discriminant (FLD) model equipped with a set of normalized Fourier descriptors (NFDs) extracted from the acquired images and the convolutional neural network (CNN) model using primary preprocessed images were built to recognize duck egg yolk types. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s). Finally, this work compared two classification methods and provided the better method for DY egg identification.

Multimodal depth and intensity face verification approach using LBP, SLF, BSIF, and LPQ local features fusion

In this paper we present a robust multimodal depth and intensity face verification approach by the scores fusion of two multimodal systems (multi-algorithms: multiple descriptors fusion and multi-sensors: depth and intensity fusion). First, we correct the rotations of the head in all 3D by Iterative Closest Point (ICP), and then we present our preprocessing algorithm. For feature extraction, we use 4 local methods Local Binary Patterns (LBP) based on the coding of a local neighborhood.Statistical Local Features (SLF), based on the calculation of some statistical parameters in a neighborhood of the pixel of interest.Binarized Statistical Image Features (BSIF) for computes a binary code for each pixel by linearly projecting local image patches onto a subspace.Local Phase Quantization (LPQ) for quantizing the Fourier transform phase in local neighborhoods. For classification, we use the normalized correlation distance after the space reducing method Principal Component Analysis (PCA) followed by Enhanced Fisher linear discriminant Model (EFM). The fusion at the scores level by a good classifier with two classes called support vector machines (SVM). Experiments are performed on the CASIA 3D and Bousphorus faces databases. The experimental results show that the local descriptor proposed (SLF) is better than all the descriptors studied. Fusion descriptor (SLF) with the descriptor BSIF + LBP gives better results for two databases. In our system, all processes are performed automatically.

Three dimensional chemometric analyses of hyperspectral images for beef tenderness forecasting

A prototype on-line hyperspectral imaging system (λ = 400–1000 nm) was developed and used to acquire images of exposed ribeye muscle on hanging beef carcasses (n = 274) at 2-day postmortem in a commercial beef packing plant. After image acquisition, a strip steak was cut from each carcass and vacuum packaged. After aging for 14 days, the steaks were cooked and Warner-Bratzler shear force values were collected as a measure of tenderness. Four different principal component analysis-based dimensionality reduction methods were implemented to reduce information redundancy in beef hyperspectral images. Textural features extracted from the 2-day hyperspectral images were modeled using Fisher's linear discriminant (FLD), support vector machines (SVM), and decision tree (DT) models to predict 14-day aged, cooked beef tenderness. Based on a true validation procedure using 101 samples, the FLD model yielded a tender certification accuracy of 86.7%. In addition, wavelengths corresponding to myoglobin and its derivatives (541, 577, and 635 nm), beef aging (541, 577, 635, 756, and 980 nm), protein (910 nm), fat (928 nm), and water (739, 756, and 988 nm) were identified.

Robust multimodal 2D and 3D face authentication using local feature fusion

In this work, we present a robust face authentication approach merging multiple descriptors and exploiting both 3D and 2D information. First, we correct the heads rotation in 3D by iterative closest point algorithm, followed by an efficient preprocessing phase. Then, we extract different features namely: multi-scale local binary patterns (MSLBP), novel statistical local features (SLF), Gabor wavelets, and scale invariant feature transform (SIFT). The principal component analysis followed by enhanced fisher linear discriminant model is used for dimensionality reduction and classification. Finally, fusion at the score level is carried out using two-class support vector machines. Extensive experiments are conducted on the CASIA 3D faces database. The evaluation of individual descriptors clearly showed the superiority of the proposed SLF features. In addition, applying the (\(\hbox {3D} + \hbox {2D}\)) multimodal score level fusion, the best result is obtained by combining the SLF with the \(\hbox {MSLBP}+\hbox {SIFT}\) descriptor yielding in an equal error rate of 0.98 % and a recognition rate of \(\hbox {RR} = 97.22\,\%\).

Emulating biological strategies for uncontrolled face recognition

Face recognition technology is of great significance for applications involving national security and crime prevention. Despite enormous progress in this field, machine-based system is still far from the goal of matching the versatility and reliability of human face recognition. In this paper, we show that a simple system designed by emulating biological strategies of human visual system can largely surpass the state-of-the-art performance on uncontrolled face recognition. In particular, the proposed system integrates dual retinal texture and color features for face representation, an incremental robust discriminant model for high level face coding, and a hierarchical cue-fusion method for similarity qualification. We demonstrate the strength of the system on the large-scale face verification task following the evaluation protocol of the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4. The results are surprisingly well: Its modules significantly outperform their state-of-the-art counterparts, such as Gabor image representation, local binary patterns, and enhanced Fisher linear discriminant model. Furthermore, applying the integrated system to the FRGC version 2 Experiment 4, the verification rate at the false acceptance rate of 0.1 percent reaches to 93.12 percent.

Enhanced Fisher linear discriminant model based face recognition using facial combined feature

We propose a combined feature Fisher classifier method for face recognition (FR). Both facial holistic and local information are used for face representation. In addition, the enhanced Fisher linear discriminant model is employed for improving the generalization capability. Experiments show that the method is not only robust enough to moderate changes of illumination, pose, and facial expression but also superior to the traditional FR methods, such as eigenfaces and Fisherfaces.

Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.

A shape- and texture-based enhanced Fisher classifier for face recognition

This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and textu...

A shape- and texture-based enhanced Fisher classifier for face recognition

This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and texture features. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image. The dimensionalities of the shape and the texture spaces are first reduced using principal component analysis, constrained by the EFM for enhanced generalization. The corresponding reduced shape and texture features are then combined through a normalization procedure to form the integrated features that are processed by the EFM for face recognition. Experimental results, using 600 face images corresponding to 200 subjects of varying illumination and facial expressions, show that (1) the integrated shape and texture features carry the most discriminating information followed in order by textures, masked images, and shape images, and (2) the new coding and face recognition method, EFC, performs the best among the eigenfaces method using L(1) or L(2) distance measure, and the Mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within-class covariance matrix. In particular, EFC achieves 98.5% recognition accuracy using only 25 features.

FEXPAC: a program for linear discriminant classification

A new program for feature extraction and two-class classification utilizing the Fisher linear discriminant function model has been developed. It is intended as a tool in multivariate data analysis, and is especially suited for the design and storage of linear classifiers from training data sets.