background and objectives: quinoa (chenopodium quinoa willd.) is a member of amaranthaceae family that originated in the andean region in bolivia, chile and peru five thousands of years and so it has a tiny and round seeds. quinoa in different combinations of food used as food, as well as how to cook like rice grains and known as the inca rice in the south american countries. the high nutrition...

Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations a...

optimal attributes are useful in interpretation of seismic data. two proposed methods are presented in this paper for finding optimal attributes. regularized discriminate analysis(rda) is based on 2 parameters ë, ? which called regularization parameter. the other method is principal component analysi s(pca).in this paper gas chimney detection is defined as the subject of study for ranking relev...

We develop a principal component analysis (PCA) for high frequency data. As in Northern fairly tales, there are trolls waiting for the explorer. The first three trolls are market microstructure noise, asynchronous sampling times, and edge effects in estimators. To get around these, a robust estimator of the spot covariance matrix is developed based on the Smoothed TSRV (Mykland et al. (2017)). ...

This paper presents a feature selection method based on the popular transformation approach: principal component analysis (PCA). It is popular because it finds the optimal solution to several objective functions (including maximum variance and minimum sum-squared-error), and also because it provides an orthogonal basis solution. However, PCA as a dimensionality reduction algorithm do not explic...

In functional linear regression, the slope “parameter” is a function. Therefore, in a nonparametric context, it is determined by an infinite number of unknowns. Its estimation involves solving an illposed problem and has points of contact with a range of methodologies, including statistical smoothing and deconvolution. The standard approach to estimating the slope function is based explicitly o...

Linear discriminant analysis (LDA) can be viewed as a twostage procedure geometrically. The first stage conducts an orthogonal and whitening transformation of the variables. The second stage involves a principal component analysis (PCA) on the transformed class means, which is intended to maximize the class separability along the principal axes. In this paper, we demonstrate that the second sta...