Two-dimensional quaternion principal component analysis (2D-QPCA) is one of the successful dimensionality reduction methods for color face recognition. However, 2D-QPCA is sensitive to outliers. For ...

This project demonstrates the use of Principal Component Analysis (PCA) to reduce the dimensionality of the classic Iris flower dataset while retaining most of the variance. PCA helps visualize ...

The majority were flavone and cinnamic acid derivatives, with group-type separations in the 2D space aiding assignment. Notably, several compounds—particularly glycosylated flavones—were reported for ...

This manuscript presents an important contribution to the field of single-cell transcriptomic analysis in cancer by introducing a novel computational framework-SCellBOW-which applies embedding ...