Discriminant analysis, including linear discriminant analysis (LDA) and quadratic (QDA), is a popular approach to classification problems. It well known that LDA suboptimal analyze heteroscedastic data, for which QDA would be an ideal tool. However, less helpful when the number of features in data set moderate or high, its variants often perform better due their robustness against dimensionalit...

PURPOSE To compare machine learning classifiers trained on three clustering schemes to determine whether distinguishing healthy eyes from those with glaucomatous optic neuropathy (GON) can be optimized by training with clustered data. METHODS Two machine learning classifiers-quadratic discriminant analysis (QDA) and support vector machines with Gaussian kernel (SVMg)-were trained separately u...

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing namely, linear discriminant analysis (LDA), quadratic (QDA), root mean square (RMS), waveform length (WL) adopted convolutional neural network (CNN), long short-term memory (LSTM). Eight-c...

background: so far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis. objective: the present study focuses on the identification of predictive biomarkers i...

In this paper, we introduce a quantum decomposition algorithm (QDA) that decomposes the problem [Formula: see text] into summation of eigenvalues times phase–space variables. One interesting feature QDA stems from its ability to simulate damped spin systems by means pure harmonic oscillators adjusted with original eigenvalue problem. We test proposed in case undriven qubit spontaneous emission ...

Linear Discriminant Analysis (LDA) is derived from the optimal Bayes classifier when classes are assumed to be Gaussian with identical covariance matrices. However, it is well known that the distribution of face images under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. The Quadratic Discriminant Analysis (QDA) which relaxes the identi...

Two airborne laser scanning datasets with leaf-on and leaf-off conditions were used to compare parameters derived from crown structure metrics and intensity data. Five deciduous species and six coniferous species were collected at the Washington Park Arboretum, Seattle, Washington, USA. Linear (LDA) and quadratic (QDA) discriminate functions were used to classify these selected species groups. ...

A commercially available Cyranose-320. conducting polymer-based electronic nose system was used to analyze the volatile organic compounds emanating from fresh beef strip loins (M. Longisimmus lumborum) stored at 4°C and 10°C. Two statistical techniques, i.e., linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), were used to develop classification models from the collect...

Quadratic discriminant analysis (QDA) is a standard tool for classification due to its simplicity and flexibility. Because the number of its parameters scales quadratically with the number of the variables, QDA is not practical, however, when the dimensionality is relatively large. To address this, we propose a novel procedure named QUDA for QDA in analyzing high-dimensional data. Formulated in...

During manufacturing and storage process, therapeutic proteins are subject to various post-translational modifications (PTMs), such as isomerization, deamidation, oxidation, disulfide bond modifications and glycosylation. Certain PTMs may affect bioactivity, stability or pharmacokinetics and pharmacodynamics profile and are therefore classified as potential critical quality attributes (pCQAs). ...