Animation Geometry Compression Using the Linear Discriminant Analysis

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

Image compression is the technique of reducing the number of bits required to represent a digital image, which can be accomplished by reducing the redundant and visually irrelevant information present in the images. In this study, a Wavelet Difference Reduction (WDR) coding based image compression technique is proposed which uses Principal Component Analysis (PCA). These techniques are combined in order to achieve high compression ratio with an acceptable degradation in the compressed images. The input image is first compressed using PCA. Few of the Principal Components are used to reconstruct the image. The reconstructed image obtained by PCA is further used as an input data for WDR compression. Since WDR is a wavelet based coding technique so it exploits local characteristics of the image and leads to high Compression Ratio (CR). Here, PCA and WDR are combined because PCA gives high image quality but low CR on the other hand WDR works opposite of it as it gives high CR. The proposed technique is applied on several test images and results are compared with WDR and JPEG2000 techniques. Results clearly show that the proposed technique is better than the existing techniques in terms of both objective and subjective fidelity criteria.

To evaluate the usefulness of differentially expressed proteins from colorectal cancer (CRC) tissues for differentiating cancer and normal tissues. A Proteomic approach was used to identify the differentially expressed proteins between CRC and normal tissues. The proteins were extracted using Tris buffer and thiourea lysis buffer (TLB) for extraction of aqueous soluble and membrane-associated proteins, respectively. Chemometrics, namely principal component analysis (PCA) and linear discriminant analysis (LDA), were used to assess the usefulness of these proteins for identifying the cancerous state of tissues. Differentially expressed proteins identified were 37 aqueous soluble proteins in Tris extracts and 24 membrane-associated proteins in TLB extracts. Based on the protein spots intensity on 2D-gel images, PCA by applying an eigenvalue > 1 was successfully used to reduce the number of principal components (PCs) into 12 and seven PCs for Tris and TLB extracts, respectively, and subsequently six PCs, respectively from both the extracts were used for LDA. The LDA classification for Tris extract showed 82.7% of original samples were correctly classified, whereas 82.7% were correctly classified for the cross-validated samples. The LDA for TLB extract showed that 78.8% of original samples and 71.2% of the cross-validated samples were correctly classified. The classification of CRC tissues by PCA and LDA provided a promising distinction between normal and cancer types. These methods can possibly be used for identification of potential biomarkers among the differentially expressed proteins identified.

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

An electronic nose (E-nose) was developed to distinguish different stored eggs by using principal component analysis (PCA),linear discriminant analysis (LDA),BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose can distinguish eggs of different storage time under cool and room-temperature storage by LDA and PCA. LDA preceded PCA. Better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices by quadratic polynomial step regression (QPSR). The prediction models indicated good prediction performance with correlation coefficient between predicted and measured values greater than 0.90.

We investigated three classifiers for the task of distinguishing between benign and malignant breast lesions. Classification performance was measured in terms of area under the ROC curve (AUC value). We compared linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and a Bayesian neural net (BNN) with 5 hidden units. For each lesion, 46 image features were extracted and principal component analysis (PCA) of these features was used as classifier input. For each classifier, the optimal number of principal components was determined by performing PCA within each step of a leave-one-case-out protocol for the training dataset (1125 lesions, 14% cancer prevalence) and determining which number of components maximized the AUC value. Subsequently, each classifier was trained on the training dataset and applied ‘cold turkey’ to an independent test set from a different population (341 lesions, 30% cancer prevalence). The optimal number of principal components for LDA was 24, accounting for 97% of the variance in the image features. For QDA and BNN, these numbers were 5 (70%) and 15 (93%), respectively. The LDA, QDA and BNN obtained AUC values of 0.88, 0.85, and 0.91, respectively, in the leave-one-case-out analysis. In the independent test – with AUCs of 0.88, 0.76, and 0.82 – only LDA achieved performance identical to that for the training set (lower bound of 95% non-inferiority interval -.0067), while the others performed significantly worse (p-values << 0.05). While the more complex BNN classifier outperformed the others in leave-one-case-out of a large dataset, LDA was the robust best-performer in an independent test.

Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.

The management and disposal of waste is a severe social issue and an essential part of ecological sustainability. As an important component of the green, low-carbon, and recycling economic system, the identification and classification of recyclable waste is the premise of its reuse and energy conservation. The main issues at hand are to improve the classification accuracy and reliability of recyclable waste and to achieve automatic classification. The methods based on physical characteristics and image-based methods are inaccurate and unreliable. The current spectroscopy methods need to process the detected samples in advance, unsuitable for automatic detection. Based on material composition properties, the Laser-Induced Breakdown Spectroscopy (LIBS) technology is here proposed to accurately and reliably identify and classify recyclable waste into six categories at the level of consumer, such as paper, plastic, glass, metal, textile, and wood. The method is also used to subclassify the same category of waste for reuse at the level of a recycling factory. We subclassified metals into iron, stainless steel, copper, and aluminum and plastics into polyvinylchloride, polyoxymethylene, acrylonitrile-butadiene-styrene, polyamide, polyethylene, and polytetrafluoroethylene. The drop-dimension methods of LIBS spectra of waste were researched to eliminate noise and redundant information by principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. Their clustering effects were analyzed to choose a suitable dimension. Combining the random forest (RF), back propagation neural network (BPNN), and convolutional neural network (CNN), we established and compared five classification models, PCA + RF, PCA + BPNN, LDA + RF, LDA + BPNN, and 1D-CNN. For the classification of six categories, the accuracies of proposed classification models are all more than 96%, and LDA(5D) + RF has 100% accuracy and optimal classification performance indices. For the subclassification of metals and plastics, PCA(8D) + RF has the highest classification accuracy of 98.77% and 99.52%, respectively.

In data analyzing and machine learning, PCA and LDA are widely used tools, these are linear transformation methods to reduce the dimension observation, and these are used in many practical applications like compression and data visualization in machine learning. Here, I have applied these two methods that are principal component analysis (PCA) and linear discriminant analysis (LDA) in handwritten digit recognition. The main motto of this paper is to present a simple and clear understanding of these methods. Here, this paper depicts that LDA can outperform PCA when training data set is huge, and PCA can outperform LDA when training data set is small.

Near infrared reflectance spectroscopy was used to discriminate different species of freshwater fish samples. Samples from seven freshwater fish species of the family Cyprinidae (black carp ( Mylopharyngodon piceus), grass carp ( Ctenopharyngodon idellus), silver carp ( Hypophthalmichthys molitrix), bighead carp ( Aristichthys nobilis), common carp ( Cyprinus carpio), crucian ( Carassius auratus), and bream ( Parabramis pekinensis)) were scanned by near infrared reflectance spectroscopy from 1000 nm to 1799 nm. Linear discriminant analysis models were built for the classification of species. We inspected the effect of partial least square, principal component analysis, competitive adaptive reweighted sampling, and fast Fourier transform on linear discriminant analysis. The results showed that the dimension reduction methods worked very well for this example. Linear discriminant analysis models which were combined with principal component analysis and fast Fourier transform could classify accurately all the samples under multiplicative scatter correction pre-processing. According to the loadings in principal component analysis, spectra wavelengths 1000, 1001, 1154, 1208, 1284, 1288, 1497, 1665, and 1770 nm were selected as effective wavelengths in linear discriminant analysis. The discriminant analysis was simplified by using effective wavelengths as independent variables in a linear discriminant analysis model. This study indicated that linear discriminant analysis combined with near infrared reflectance spectroscopy could be an effective strategy for the classification of freshwater fish species.

Electroencephalogram (EEG) signal-processing techniques are the prominent role in the detection and prediction of epileptic seizures. The detection of epileptic activity is cumbersome and needs a detailed analysis of the EEG data. Therefore, an efficient method for classifying EEG data is required. In this work, a constructive pattern recognition strategy for analysing EEG data as normal and epileptic seizure has been proposed. With this strategy, the signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to reduce the dimensionality of EEG data. These reduced features were used as input to Naïve Bayes and K-Nearest Neighbour Classifier to classify normal or epileptic seizure signal. The performance of classifier was evaluated in terms of accuracy, sensitivity and specificity. The experimental results show that PCA with Naïve Bayes classifier provides 98.6% accuracy and LDA with Naïve Bayes classifier attains improved result of 99.8% accuracy. Also, the result shows that PCA, LDA with K-NN achieves 98.5% and 100% accuracy. This evaluation is used to propose a reliable, practical epilepsy detection method to enhance the patient’s care and quality of life.

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural network classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available. The proposed method was tested on ORL face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Peony seed oil has recently been introduced as a high-quality food oil. Because the high price of peony seed oil may tempt unscrupulous merchants to dilute it with cheaper substitutes, a rapid detection method for likely adulterants is required. In this study, the fatty acid composition of peony seed oil and four less expensive edible oils (soybean oil, corn oil, sunflower oil, and rapeseed oil) were measured by gas chromatography mass spectrometry. Peony oil adulterated by other edible oils was assessed using iodine values to estimate the extent of adulteration. Adulteration was also measured using an electronic nose (E-nose) combined with principal component analysis (PCA) or linear discriminant analysis (LDA). Results indicated that peony seed oil was highly enriched in α-linolenic acid. Although the iodine value can be used to detect some adulterants by measuring unsaturation, it was not able to detect all four potential adulterants. In contrast, the E-nose can rapidly identify adulterated peony seed oil by sampling vapor. Data analyses using PCA and LDA show that LDA more effectively clusters the data, discriminates between pure and adulterated oil, and can detect adulteration at the 10% level. E-nose combined with LDA suitable for detection of peony seed oil adulteration.