Principal component analysis has been widely adopted to reduce the dimension of data while preserving information. The quantum version PCA (qPCA) can be used analyze an unknown low-rank density matrix by rapidly revealing principal components it, i.e. eigenvectors with largest eigenvalues. However, due substantial resource requirement, its experimental implementation remains challenging. Here, ...

background: fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. by early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant. objective: here, we extract fetal ecg from maternal abdominal recordings and detect r-peaks in order to recognize fetal heart rate. on the next step, we find a b...

the objective of this study was to investigate the relationship among body weight and fat-tail measurements with fat-tail weight by using multiple regression and principal component analysis. the eleven characters includes, body weight, fat-tail length, fat-tail circumference, width and diameters in 3 position of upper, middle and lower before the slaughter, and also fat-tail weight after the s...

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...