This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA ...focuses on selecting the localized features from the expression frames using the partial F-test values, thereby reducing the within class variance and increasing the low between variance among different expression classes. For recognition, the hidden conditional random fields (HCRFs) model is utilized. HCRF is capable of approximating a complex distribution using a mixture of Gaussian density functions. To achieve optimum results, the system employs a hierarchical recognition strategy. Under these settings, expressions are divided into three categories based on parts of the face that contribute most toward an expression. During recognition, at the first level, SWLDA and HCRF are employed to recognize the expression category; whereas, at the second level, the label for the expression within the recognized category is determined using a separate set of SWLDA and HCRF, trained just for that category. In order to validate the system, four publicly available data sets were used, and a total of four experiments were performed. The weighted average recognition rate for the proposed FER approach was 96.37% across the four different data sets, which is a significant improvement in contrast to the existing FER methods.
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► Classification of various neocryptolepine derivatives according to their anti-malarial activity. ► Use of LDA, QDA, CART, PLS-DA, OPLS-DA, OAO-SVM-C, and OAA-SVM-C for ...classification. ► CART model preferred for three-class classification according to activity. ► LDA and QDA models preferred for two-class classification according to activity.
This paper describes the construction of a QSAR model to relate the structures of various derivatives of neocryptolepine to their anti-malarial activities. QSAR classification models were build using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART), Partial Least Squares – Discriminant Analysis (PLS-DA), Orthogonal Projections to Latent Structures – Discriminant Analysis (OPLS-DA), and Support Vector Machines for Classification (SVM-C), using four sets of molecular descriptors as explanatory variables. Prior to classification, the molecules were divided into a training and a test set using the duplex algorithm. The different classification models were compared regarding their predictive ability, simplicity, and interpretability. Both binary and multi-class classification models were constructed. For classification into three classes, CART and One-Against-One (OAO)-SVM-C were found to be the best predictive methods, while for classification into two classes, LDA, QDA and CART were.
Using confocal Raman micro‐spectroscopy, this study aims to elucidate the cellular responses of the γ‐secretase inhibitor, N‐N‐(3,5‐difluorophenacetyl)‐L‐alanyl‐S‐phenylglycine t‐butyl ester (DAPT), ...in osteosarcoma (OS) cells in a dose‐ and time‐dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares‐discriminant analysis (PLS‐DA) and principal component analysis‐linear discriminant analysis (PCA‐LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave‐one‐out cross validation method, it is shown that PLS‐DA exhibited more classification capacity than PCA‐LDA algorithm. Hence, by understanding the DAPT‐based cellular variations, the achieved results provided an experimental foundation to establish new DAPT‐based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug‐cell interactions.
The dose‐ and time‐dependent responses of the γ‐secretase inhibitor (DAPT) in osteosarcoma cells were illustrated by Raman microspectroscopy. Main compositional changes (nucleic acids, protein, and lipid) in DAPT treated K7M2 cell line were fully addressed after detailed spectral analyses, testifying inhibitory effects of DAPT on the cell proliferation. Both PLS‐DA and PCA‐LDA analyses revealed governing compositional variations among investigated groups by distinguishing their spectral characteristics with relatively high accuracies.
A negatively charged poly(para‐phenyleneethynylene) (PPE) forms electrostatic complexes with four positively charged antimicrobial peptides (AMP). The AMPs partially quench the fluorescence of the ...PPE and discriminate fourteen different bacteria in water and in human urine by pattern‐based fluorescence recognition; the AMP‐PPE complexes bind differentially to the components of bacterial surfaces. The bacterial species and strains form clusters according to staining properties (Gram‐positive and Gram‐negative) or genetic similarity (genus, species, and strain). The identification and data treatment is performed by pattern evaluation with linear discriminant analysis (LDA) of the collected fluorescence intensity data.
Discriminated! Electrostatic complexes formed from four cationic antimicrobial peptides (AMPs) and one anionic poly(para‐phenylene‐ethynylene) (PPE) were examined as a new type of an array‐based sensor. The array identifies and discriminates 14 different types of bacteria according to Gram status and their genetic relationship in human urine by subjecting the obtained fluorescence response patterns to linear discriminant analysis.
In this brief, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) problem is proposed, and a self-weighted supervised discriminative feature selection (SSD-FS) method is derived by ...introducing sparsity-inducing regularization to the proposed SOLDA problem. By using the row-sparse projection, the proposed SSD-FS method is superior to multiple sparse feature selection approaches, which can overly suppress the nonzero rows such that the associated features are insufficient for selection. More specifically, the orthogonal constraint ensures the minimal number of selectable features for the proposed SSD-FS method. In addition, the proposed feature selection method is able to harness the discriminant power such that the discriminative features are selected. Consequently, the effectiveness of the proposed SSD-FS method is validated theoretically and experimentally.
This paper analyzes the classification of hyperspectral remote sensing images with linear discriminant analysis (LDA) in the presence of a small ratio between the number of training samples and the ...number of spectral features. In these particular ill-posed problems, a reliable LDA requires one to introduce regularization for problem solving. Nonetheless, in such a challenging scenario, the resulting regularized LDA (RLDA) is highly sensitive to the tuning of the regularization parameter. In this context, we introduce in the remote sensing community an efficient version of the RLDA recently presented by Ye to cope with critical ill-posed problems. In addition, several LDA-based classifiers (i.e., penalized LDA, orthogonal LDA, and uncorrelated LDA) are compared theoretically and experimentally with the standard LDA and the RLDA. Method differences are highlighted through toy examples and are exhaustively tested on several ill-posed problems related to the classification of hyperspectral remote sensing images. Experimental results confirm the effectiveness of the presented RLDA technique and point out the main properties of other analyzed LDA techniques in critical ill-posed hyperspectral image classification problems.
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where ...p,n → ∞ and p/n → γ > 0, and allow for arbitrary covariance among the features. For both methods, we provide an explicit and efficiently computable expression for the limiting predictive risk, which depends only on the spectrum of the feature-covariance matrix, the signal strength and the aspect ratio γ. Especially in the case of regularized discriminant analysis, we find that predictive accuracy has a nuanced dependence on the eigenvalue distribution of the covariance matrix, suggesting that analyses based on the operator norm of the covariance matrix may not be sharp. Our results also uncover an exact inverse relation between the limiting predictive risk and the limiting estimation risk in high-dimensional linear models. The analysis builds on recent advances in random matrix theory.
Two‐dimensional linear discriminant analysis (2DLDA) is a widely applied extension of LDA that can cope with matrix input samples directly. However, its construction is based on a squared F $F$‐norm ...which will lead to sensitivity to noise and outliers. In this paper, a square‐free F $F$‐norm 2DLDA is proposed to improve the robustness of 2DLDA. By losing the squared operation, the proposed method weakens the influence of outliers and noise and at the same time keeps the geometric structure of data. It can be solved through an effective nongreedy iterative algorithm, with each subproblem having a closed‐form solution. The algorithm is further proved to be convergent. Experiments on several human face image databases demonstrate the effectiveness and robustness of the proposed method.
Brain computer interface translates electroencephalogram (EEG) signals into control commands so that paralyzed people can control assistive devices. This human thought translation is a very ...challenging process as EEG signals contain noise. For noise removal, a bandpass filter or a filter bank is used. However, these techniques also remove useful information from the signal. Furthermore, after feature extraction, there are such features which do not play any significant role in effective classification. Thus, soft computing-based EEG classification followed by extraction and then selection of optimal features can produce better results. In this paper, subband common spatial patterns using sequential backward floating selection is being proposed in order to classify motor-imagery-based EEG signals. The signal is decomposed into subband using a filter bank having overlapped frequency cutoffs. Linear discriminant analysis followed by common spatial pattern is applied to the output of each filter for features extraction. Then, sequential backward floating selection is applied for selection of optimal features to train radial basis function neural networks. Two different datasets have been used for evaluation of results, i.e., Open BCI dataset and EEG signals acquired by Emotiv Epoc. The proposed system shows an overall accuracy of 93.05% and 85.00% for both datasets, respectively. The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective method for EEG classification as compared to previous techniques.
Surface enhanced Raman spectroscopy (SERS) was used to determine the biochemical changes induced during the antibacterial activity of the in house synthesized imidazole derivatives in comparison to ...the commercially available pharmaceutical tinidazole. The antibacterial activity was assessed against Bacillus subtilis using various concentrations of antibacterial agents for this purpose. SERS detected significant changes in the bacterial cells following the application of both drugs, confirming the potential of this approach for analyzing the antibacterial activities of drug candidates. The SERS spectral features changed consistently with the increased antibiotic concentration at 548, 713, 730, 807, 858, 951, 958, 1004, 1028, 1110, 1128, 1242, 1280, 1320, 1398, 1418, 1453, 1567, and 1590 cm
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. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were demonstrated to be useful for comparing the antibacterial actions of these imidazole derivatives. The sensitivity and specificity of this model were 97% and 95.7% respectively.
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